Traditional methods in microbiology rely on viable plate counting to enumerate bacteria and often underestimate true cell counts in biofilms because matrix-encased aggregates can yield a single colony despite containing many cells. This limitation skews CFU/mL measurements in in vitro assays evaluating antimicrobials. Here, we present BacQuant, a computer vision pipeline to detect and quantify biofilm aggregates from brightfield microscopy images. BacQuant distinguishes individual cells from aggregates and provides improved cell counts. Automated counts matched manual microscopic counts and exceeded viable plate counts, highlighting shortcomings of conventional methods and offering a scalable, inexpensive alternative for biofilm quantification.
","acknowledgements":"","authors":[{"affiliations":["Binghamton University","Binghamton University"],"departments":["First-year Research Immersion Program","Department of Biological Sciences"],"credit":["conceptualization","dataCuration","formalAnalysis","methodology","writing_originalDraft"],"email":"tevinflom@gmail.com","firstName":"Tevin","lastName":"Flom","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","supervision","writing_reviewEditing"],"email":"uciftci@binghamton.edu","firstName":"Umur A. ","lastName":"Ciftci","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program,"],"credit":["conceptualization","methodology","resources","supervision","writing_reviewEditing"],"email":"clight@binghamton.edu","firstName":"Caitlin J. ","lastName":"Light","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","fundingAcquisition","methodology","project","resources","supervision","writing_originalDraft","writing_reviewEditing","validation"],"email":"wredman@binghamton.edu","firstName":"Whitni K.","lastName":"Redman","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":null,"extendedData":[],"funding":"This work was supported by the First-year Research Immersion Program at Binghamton University, the Binghamton University Scholars Program, and the Harpur’s Edge Award from the External Scholarship and Undergraduate Research Center at Binghamton University.
","image":{"url":"https://portal.micropublication.org/uploads/935caddb05de35388f3efffa11e36f66.jpeg"},"imageCaption":"(A) Biofilm growth, sample preparation, and analysis. 48-hour PAO1 biofilms were collected, rinsed to removed unattached cells, and sonicated to liberate biofilm-encased bacteria. The resulting suspension were mounted on slides, stained with safranin, and imaged by brightfield microscopy. Images were analyzed using the BacQuant pipeline to distinguish free cells from aggregates and determine cell counts. In parallel, cell viability was assessed by serial dilution and plating to obtain CFU/mL measurements. (B) Representative images of stained cell populations. Cells were stained with a safranin-based counterstain for one minute in order to show both free cell and aggregate morphologies. Examples of free cells (A) and aggregates (B) are shown by the labeled arrows. These images were typical of the aforementioned experimental design. Representative of n= 32 images. Scale bars represent 10 µm. (C,D) Validation of the Pipeline Against Ground-Truth Labels. Estimation plots of free cells (C) and aggregates (D) are shown. Ground truth counts were obtained by manually counting the number of free cells and aggregates in each image. The ground truth counts across 10 representative images from the total experimental set of 300 images were measured, and the pipeline was run once on each sample image. The green circles represent ground truth counts, and orange circles represent BacQuant measurements. (E,F) Comparison of BacQuant Against Viable Plate Cell Counts. Viable plate counts were determined via serial dilution and drop-plating of the post-sonicated sample. Representative images of drop-plates and corresponding microscopy images (E), along with the CFU/mL calculated from BacQuant (left column) and viable plate count (right column). Average cells/mL counts from BacQuant (orange bar) were compared against CFU/mL counts from viable plate counts (blue) in (F). The estimation plots above were generated from paired t-tests.; ****, p<0.0001, n=10. Statistical significance was determined with an unpaired t-test, and error bars represent standard deviation. ****, p<0.0001, n=18-22 biofilm samples with 10 images per sample.
","imageTitle":"Work Flow and Representative Results of BacQuant
","methods":"Materials and Methods
Bacterial Culture Preparation
P. aeruginosa PAO1 liquid cultures were routinely grown in lysogeny broth (LB) (SigmaAldrich®, Cat# L3022) at 37°C under 220 RPM shaking conditions for 18 hours in 125 mL flasks.
In vitro Well-plate Model
P. aeruginosa biofilms were cultivated in 24-well non-tissue culture-treated plates (VWR, Cat# 10861-558) for 48 hours at 37°C under 80 RPM shaking conditions. Individual wells were inoculated with 106 CFU/mL in 800 µL. After the 48-hour incubation, media was removed, and each well was rinsed with 1 mL of 0.85% saline solution to remove any unattached or lysed cells. This growth protocol was adapted from Fleming et. al (Fleming et al., 2020). The entire plate was covered with parafilm and placed into a water bath sonicator (Fischer Scientific, Cat# 15337411) for 30 minutes. After sonication, the contents of each well were moved to a respective 1.5 mL microcentrifuge tube.
Slide Preparation and Viability Assay
After harvesting, 10 µL of cell solution was added onto a glass microscope slide (Globe Scientific, Inc, Cat# 1304). The solution was spread into a 1 cm2 area at the center of each slide using an inoculation loop. The samples were then heat-fixed onto the slides and stained with Safranin Advanced Counterstain (Hardy Diagnostics, Cat# GK400) for 1 minute and subsequently rinsed with deionized water. The slides were then wet-mounted using immersion oil and topped with a glass cover slip. Concurrently, the remaining cell solution was serially diluted in saline and drop-plated onto solid LB agar and incubated for 18-24 hours at 37°C for CFU enumeration.
Brightfield Microscopy
All slides were imaged using an Olympus BX43 microscope under oil immersion at 100x magnification. Five representative images of each slide were taken, representing one well in the 24-well plate model. An Olympus DP22 camera was used with CellSens Entry software to take the images. The size of each image and light intensity was constant across all images. The dimensions of each image were measured to be 5000 x 7000 nm, in metric units using the Linear Ruler tool in CellSens Entry. Images were saved and label-matched to their corresponding drop-plate.
Image Analysis
A custom computer vision image analysis pipeline was built using Python v3.8 programming language. A stand-alone version of the pipeline is provided and can be run as an online notebook in or as a script with a Python IDE. All necessary interaction with the code is clearly labeled for the user as comments in the code, as this pipeline is not based on a graphical user interface (GUI). This pipeline should be adapted for individual experiments, as it is currently optimized for use with P. aeruginosa mono-species biofilm cells. All parameters are clearly labeled in the code and are easily customizable. All code is freely available, with the implementation of each step of the pipeline clearly shown. The code is available through Google Colaboratory (https://colab.research.google.com/drive/1JWpmjoXKtgYmeux7MUyBH6syZLHm1inf?usp=sharing). We recommend this pipeline be used with GPU acceleration for high-throughput analyses, but running the process in the cloud or with a CPU is also easily supported. The pipeline takes .png file format images as the primary input, as well as user-specified parameters such as the area of an individual cell in pixels and image size.
The first step of the pipeline entails generating a binary mask of the image based on color threshold values. The mask eliminates background noise in the image and standardizes the shape and sizes of all image features. Following masking, a diameter-based size thresholding algorithm is used to label large connected regions in the image as aggregates in blue, and smaller regions as free cells in yellow. This simplifies the visual distinction between aggregates and free cells. From this color-changing step, the percentages of aggregated biomass and free cells are determined via color-based pixel pooling.
Concurrently, a contour detection algorithm from the OpenCV (https://github.com/itseez/opencv) library is used to extract aggregated regions of cells as features. The edges of each aggregate are separated from the background image using Canny edge detection (DOI: 10.1109/TPAMI.1986.4767851). Then, the remaining regions are enclosed by a boundary line through contour detection. This allows for the thorough definition of the 2D shape of each aggregate. From these contours, the area of each aggregate is calculated in pixels and converted to square microns. Cell counts within the aggregates are based on the area of an individual cell in pixels, which is defined by the user at the beginning of the pipeline. It is important to note that this process only works on two-dimensional images. It also can be used with epifluorescence microscopy and live/dead staining with modification in the image processing code to exclude dead cells. It is not optimized for Z-stack images, though it can be modified to work with image arrays and high-throughput imaging results. This makes BacQuant a useful tool for more basic laboratories without complex equipment, software, or imaging capabilities.
The output of this pipeline includes the percent aggregated biomass in each image, number of aggregates, estimated number of cells in aggregates, total estimated cells in each image, and estimated calculation of cells per mL of solution. The raw data from this analysis is stored as a .csv file format spreadsheet. We recommend that further processing of the data be conducted in Microsoft Excel.
Statistical Analysis
Statistical analysis was conducted using GraphPad Prism. A paired t-test was utilized to compare ground truth and BacQuant counts of aggregates and free cells. An unpaired t-test was used to compare viable plate count assay results with BacQuant results. All analyses were conducted to determine statistical significance with an alpha value of 0.05.
","reagents":"","patternDescription":"INTRODUCTION:
Biofilms are communities of bacteria encased in a self-secreted matrix composed of extracellular polymeric substances (EPS) including lipids, proteins, extracellular DNA, and polysaccharides(Flemming et al., 2025). These microbial communities play a key role in a variety of environments, including medical, industrial, and environmental settings. It is estimated that across all major economic sectors, biofilm-associated damage costs roughly 4 trillion dollars per year globally(Camara et al., 2022). Biofilms represent a large burden in clinical practice, as nearly 80% of all human infections are associated with biofilms(Fedorowski, Moller, & Melander, 2013). Biofilm-associated infections are difficult to treat due to the protective matrix that shelters microorganisms from antibiotics and the host immune response. Both gram-positive organisms, like Staphylococcus aureus, and gram-negatives, like Pseudomonas aeruginosa, contribute to these infections and express mechanisms of resistance and persistence in biofilm communities, further complicating the treatment process(Schaber et al., 2007; Usui, Yoshii, Thiriet-Rupert, Ghigo, & Beloin, 2023). The structural and functional complexity of biofilm communities, as defined by Stoodley et. al(Stoodley, Sauer, Davies, & Costerton, 2002), underscores their role in chronic infection and treatment failures.
During the process of developing novel therapeutics to combat these infections, researchers face the same challenge time and time again which often yield high variability and lack of reproducibility of their results. Upon treating a biofilm in vitro, traditional methods for bacterial cell counting, such as colony-forming unit (CFU) counts are used to determine the number of cells that remain within the biofilm. Previous studies have shown that CFU-based counting methods significantly underestimate bacterial counts (Beal et al., 2020; Martini, Boddu, Nemenman, & Vega, 2024). Unfortunately, these methods often fail to provide accurate assessments of biofilm populations because standard culturing methods release both individual cells and EPS-bound aggregates. When those cells are counted by the conventional method, viable plate counting (VPC), both aggregates and free cells show up as a single colony on the agar plate, leading to incorrect CFU values that show lower bacterial load. This generally leads to an underestimation of viable cells in the original biofilm sample, thereby skewing experimental results and leading to inaccurate conclusions about biofilm resilience, growth, and efficacy of antimicrobial treatments.
There are two main pathways that researchers conventionally use to overcome this problem in the laboratory – homogenization and water bath sonication(Buckingham-Meyer et al., 2022). Homogenization involves collection of the biofilm population and submersion into a high-speed propeller that breaks up any remaining EPS through mechanical torsion(Stokell, Khan, & Steck, 2014). On the other hand, sonication uses high-frequency sound waves to produce microbubbles in the bulk liquid that break up the EPS(Bjerkan, Witso, & Bergh, 2009). Following both of these methods, the resulting cell solution is drop-plated and counted using VPC. Both methods do not exclusively yield free individual cells, but instead result in a mix of both free cells and biofilm aggregates, which represents a significant hindrance to estimating the true population size in a biofilm sample.
Recent advancements in the field of computer vision and image processing yield promising solutions for matrix-encased bacterial quantification. Digital microscopy, alongside image analysis techniques like edge and contour detection for segmentation, offers a novel approach for differentiating between individual cells and aggregates in biofilm samples. Previous studies have demonstrated the usefulness of image processing in microbial research in identifying complex morphological and structural features of biofilms(Holicheva et al., 2025; J. Wang et al., 2022). Current technologies such as BiofilmQ use cytometry and spatial microscopy to map water channels, scaffolds, and other structural features within biofilms, but it does not center on counting cell populations(Hartmann et al., 2021). Work done by Mountcastle et. al using computer vision and the ImageJ software to enumerate biofilm bacteria from live/dead staining also relies on confocal microscopy to determine viable cells, but focuses on in situ counting rather than experimental counting of a population post-liberation from the biofilm(Mountcastle et al., 2021). These tools are rather bulky and take up considerable resources on a personal computer. They require the installation of entire software packages, like ImageJ, and do not readily work on simple light micrographs. Additionally, complex methods such as flow cytometry and confocal microscopy are difficult to perform and present significant monetary expense for researchers.
This study presents BacQuant, a computationally inexpensive image processing pipeline to improve quantification of biofilm cells following sonication. We used the bacterial species P. aeruginosa as a model biofilm-former for this study. Among biofilm-forming bacteria, P. aeruginosa is a well-studied organism due to its relevance in human infection, specifically in patients with immune-compromising comorbidities like diabetes and cystic fibrosis. P. aeruginosa can commonly be found in infections including diabetic foot ulcers, burn wounds, conjunctivitis, and the mucosa of cystic fibrosis patients(Acosta et al., 2020; Fowler, Bloomquist, Sakhalkar, & Bloomquist, 2023; Goldufsky et al., 2015; Gonzalez et al., 2016). The persistence of P. aeruginosa biofilms in clinical settings poses significant challenges for the eradication of biofilm-associated infections.
P. aeruginosa biofilms were grown in a 24-well plate, sonicated in saline to disaggregate the cells. The resultant suspension was stained and imaged using oil-immersion phase contrast microscopy. The images acquired through this technique were analyzed through a custom pipeline to detect individual cells, aggregates, the number of cells in aggregates, and the total cell count. By leveraging image processing techniques, this method provides a more precise estimation of biofilm cell counts compared to traditional VPC(Klinger-Strobel, Suesse, Fischer, Pletz, & Makarewicz, 2016) (Figure 1). It is important to note that BacQuant does not intend to count viable cells. Rather, it estimates the total cell count in the biofilm sample including both live and dead cells. It does not estimate live cells like traditional VPC methods do.
By integrating computer vision and quantitative microscopy, this study bridges the gap between traditional methods in wet lab microbiology and contemporary digital analytical tools. As biofilms continue to pose challenges in medicine, industry, and research, analytical techniques for enumeration to accurately assess composition and cell phenotypes is an essential innovation for biofilm treatment strategies and management.
RESULTS
To assess the relative accuracy of the automated cell counting experiment presented in Figure 1A, a series of comparisons between the output of the BacQuant pipeline, hand-calculated cell counts, and viable plate cell counts was performed.
We found that the BacQuant pipeline significantly underestimated free cell counts in a given image, but performed well in estimating the number of aggregates. Additionally, BacQuant measured the total cell count of a biofilm sample nearly five orders of magnitude greater than cell counts determined through viable cell count assays.
Definition of Free Cells and Aggregates from Phase-Contrast Brightfield Micrographs
Sonication of 24-well plate P. aeruginosa biofilms consistently yields two distinct morphologies – free individual cells liberated from the biofilm matrix and biofilm aggregates, chunks of cells or microcolonies still encased in an EPS matrix after mechanical disruption of the biofilm. In order to define what biofilm aggregates and free cells look like to the naked eye, representative images of sonicated biofilm populations are shown in Figure 1B. Free cells tend to be consistent in their dimensions, but aggregates can vary tremendously in their size and shape. Generally, they are approximately circular with jagged edges and can measure up to 1000 µm at their widest point. When stained with safranin, aggregates tend to appear more saturated in color due to the presence of both EPS and bacterial cells. Sonication consistently produces both morphologies, with no observation of any biofilm samples that exclusively exhibit one or the other.
BacQuant Aggregate Counts Does Not Differ From Ground Truth
Ground truth labels, or hand-counted estimations of the cell population within an image, were used to validate the performance of the BacQuant pipeline. Ground truth image measurements were obtained by counting the number of individual cells and aggregates across ten representative images. The images were chosen based on their quality. All images contained a two-dimensional view of all of the free cells and aggregates with no partial aggregates on the corners or edges of the image outside of the view of the microscope camera.
Figures 1C,D show an estimation plot for the image set. Each image was treated as a biological replicate, wherein the same image was hand-counted and fed through BacQuant to draw a comparison. The estimation plots show the difference between the number of free cells and aggregates counted by hand and by BacQuant, respectively. Figure 1C, which represents the difference in the number of free cells in each image, shows a statistically significant difference (p=0.0001) between the free cells identified by hand and those identified by BacQuant. The pipeline seems to underestimate the number of free cells in each image when compared to ground truth measurements. This is likely due to the variation in cell size with two-dimensional images because there is no reliable way to ensure cells are in the same spatial orientation. Moreover, free cell count is determined by pixel counting and normalization to a user-specified average cell size, leading to underestimation. However, it seems that BacQuant slightly overestimates the number of aggregates when compared to ground truth counts, although not significant (p=0.0569). (Figure 1D). This trend likely comes from the structure of the code used to generate these results, where edge detection is utilized in two steps of the pipeline, and the ultimate edge detection step yields a more conservative segmentation result than the first. Additionally, the depth of the aggregates is not measured by this system, so aggregated cell counts may still be underestimated.
BacQuant Significantly Enumerated More Cells Than Viable Cell Counts
In order to gauge the difference between BacQuant cell counts and conventional methods of cell counting in microbiology, a comparison between BacQuant total cell counts and viable plate counts was performed. Viable plate counts were obtained using a serial dilution and drop-plating to estimate the total cell count across biofilm samples (Figure 1A). Viable plate counts were measured by counting the number of colonies that grew on each countable dilution factor on the drop-plate (Figure 1E).
The average cell count across twenty samples was calculated and represented in the bar graph comparing the counts between the viability plates and BacQuant image-based analysis. The average cell count determined by viable plate count was significantly lower (p=0.0001) than that obtained by BacQuant with 4.46 x 107 and 1.18 x 1011, respectively (Figure 1F). The data obtained by BacQuant were highly clustered together, and the viability data was more variable. This is likely due to the presence of multiple cells in one colony progenated by a given biofilm aggregate, which contain many cells but only yield a single colony on an agar plate. This can lead to underestimation of the total viable cell count. Additionally, BacQuant cannot distinguish between live and dead cells, perhaps overestimating the total viable cell count.
DISCUSSION:
This study presents a novel image processing pipeline designed to enhance the quantification of biofilm aggregates following mechanical disruption by sonication in an easy-to-use, resource-efficient manner. Our approach builds on conventional methods in microbiology, namely CFU/mL viability assays that entail serial dilution and drop-plating onto agar medium (Figure 1A). BacQuant improves the resolution and accuracy of conventional methods to enumerate bacterial populations by accounting for residual biofilm aggregates that remain after mechanical disruption that has been shown to impact the validity of viable cell counts(Fleming et al., 2020). By integrating thresholding and edge/contour detection, we achieved an instance segmentation result that offers a reproducible and scalable method to distinguish individual bacterial cells from biofilm aggregates in a sample as a potential method to improve bacterial counts.
For this study, sonication was chosen over homogenization as the representative method of mechanical torsion to detach biofilms due to its widespread use in industrial applications like removing biofilms from HVAC systems, clinical applications like oral and wound care, and reproducibility in laboratory environments. While sonication is widely used to disaggregate and dislodge biofilms from surfaces, we show that it does not always break up biofilms into their component cells, and instead leaves aggregates of cells still bound by the biofilm matrix(Kragh et al., 2016) (Figure 1B). These aggregates are often represented as single colonies when grown on agar plates, just like individual cells, which leads to underestimation of true viable bacterial cell count. BacQuant provides a way to discriminate aggregates after a cell counting wet-lab experiment and estimates the number of cells in each aggregate, yielding a more accurate estimate of bacterial load. As seen in Figure 1F, BacQuant produces counts that are significantly higher than viable plate counts. This aligns with prior concerns in the literature regarding the reliability of CFU based counting methods, specifically when evaluating biofilm growth on a surface. The data from BacQuant are also more tightly clustered together than the correlating VPC data. It is important to note that CFU plating typically results in wider error due to volume and dilution human errors that BacQuant does not encounter. Different methods of counting and recording drop plates, such as counting the highest and lowest dilutions, averaging all countable dilutions, and counting dilutions that number within certain acceptable boundaries, are not standardized and are typically utilized at the discretion of individual laboratories, which can skew results in myriad ways.
A key strength of our pipeline lies in its adaptability to different types of experiments and its automation. Unlike methods that require extensive training data sets or object detection algorithms and other machine learning approaches, our pipeline is lightweight and broadly accessible. It does not require hardware acceleration and can be easily run on any CPU on any modern computer. It can be adjusted to accommodate many different imaging conditions and magnification levels, supporting a wide range of light-microscopy workflows. Most importantly, it captures biologically meaningful distinctions between aggregates and single cells, which is routinely missed by conventional approaches.
However, our approach is not without limitations. Over-segmentation is a concern in densely populated images, where adjacent cells may be incorrectly counted as aggregated clusters. This becomes apparent in Figure 1D, as BacQuant slightly overestimates the number of aggregates in each image, though the difference is not statistically significant. The same is true for large single cells or actively dividing cells, but the problem is largely remedied by the structure of the code, where a circle of a certain diameter is passed over the image to classify aggregates. An additional safeguard is provided by contour detection, where contours that are too small are excluded from the analysis. Conversely, in Figure 1C, BacQuant significantly underestimates the number of individual cells in the image when compared to ground truth labels. This underestimation is likely due to the user-defined constant variable for single-cell area in pixels. This input should be an average estimate of cell size, but the pipeline does not take into account the orientation or individual morphology of the free cells, which may be above or below the average cell size. Therefore, pixel-based labeling of cells may lead to underestimating the actual number of free cells in the image. It is paramount to note that BacQuant cannot distinguish between live and dead cells, which may also skew cell counts.
The success of BacQuant is also contingent upon high-contrast images with minimal background noise, which are hard to come by in clinical settings and may require further preprocessing steps. The estimation of total cell count relies on two assumptions: a.) that the entire slide looks like the representative images used; and b.) that the sample was heat-fixed to the microscope slide in a 1 cm2 square area. The former is true of all image-based analyses, while the latter depends on the user’s correct implementation of the experimental protocol. Calibration parameters in the code must be changed and adjusted for each individual experiment and lighting conditions in and around the microscope.
Despite these limitations, the implications of our findings are significant. Accurate quantification of biofilm bacteria is critical in settings ranging from antimicrobial susceptibility testing to environmental monitoring. BacQuant offers a scalable, computationally inexpensive complement to traditional microbiology methods, providing more detailed insights into bacterial population structure. The user does not need to install bulky software, learn a new interface, or spend much time with this analysis. The entire analysis can be completed within just a few minutes of sample collection, which is in stark contrast to current methods that can take several hours or even days to see results. In clinical research, this could aid in evaluating biofilm resilience in chronic infections(Folliero et al., 2021). In industrial settings, this could support more accurate assessments of disinfection strategies. Meanwhile, in environmental testing, this method could be used to ascertain the lifestyles of soil bacteria and bacterial contaminants.
Future work should focus on expanding the pipeline’s classification capabilities through integration with unsupervised machine learning, deep learning object detection, and AI-assisted labeling of more discrete morphological traits. BacQuant should also be expanded to work with three-dimensional images from confocal or electron microscopy for higher-fidelity examination of 3D biofilm structures. Correlating image-based quantification with molecular markers of viability such as live/dead staining or qPCR could expand the power of this system, perhaps inviting insights about persister cells, metabolic stratification, water channels, and other complex features of the biofilm lifestyle(Sauer et al., 2022; Wang, Zhu, Zheng, Dong, & Liu, 2022). Similar computer vision methods should be utilized with live/dead stained cell images in order to yield better estimations of viable cells in a population for applications like evaluating antimicrobial efficacy. Ultimately, BacQuant could serve as a foundation for a more robust, automated framework for quantifying biofilm bacteria, generating new insights into how structure and function relate to each other in biofilm environments.
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","pubmedId":"","doi":"10.1128/IAI.00586-07"},{"reference":"Stoodley P, Sauer K, Davies DG, Costerton JW. 2002. Biofilms as Complex Differentiated Communities. Annual Review of Microbiology 56: 187-209.
","pubmedId":"","doi":"10.1146/annurev.micro.56.012302.160705"},{"reference":"Usui M, Yoshii Y, Thiriet-Rupert S, Ghigo JM, Beloin C. 2023. Intermittent antibiotic treatment of bacterial biofilms favors the rapid evolution of resistance. Communications Biology 6: 10.1038/s42003-023-04601-y.
","pubmedId":"","doi":"10.1038/s42003-023-04601-y"},{"reference":"Wang J, Tabassum N, Toma TT, Wang Y, Gahlmann A, Acton ST. 2022. 3D GAN image synthesis and dataset quality assessment for bacterial biofilm. Bioinformatics 38: 4598-4604.
","pubmedId":"","doi":"10.1093/bioinformatics/btac529"},{"reference":"Wang S, Zhu H, Zheng G, Dong F, Liu C. 2022. Dynamic Changes in Biofilm Structures under Dynamic Flow Conditions. Applied and Environmental Microbiology 88: 10.1128/aem.01072-22.
","pubmedId":"","doi":"10.1128/aem.01072-22"}],"title":"BacQuant: A Scalable Automated Image Processing Pipeline for Quantifying Biofilm Aggregates
","reviews":[],"curatorReviews":[]},{"id":"9f79851a-590d-4f59-ad7f-d34cd3ec9661","decision":"revise","abstract":"Traditional methods in microbiology rely on viable plate counting to enumerate bacteria and often underestimate true cell counts in biofilms because matrix-encased aggregates can yield a single colony despite containing many cells. This limitation skews CFU/mL measurements in in vitro assays evaluating antimicrobials. Here, we present BacQuant, a computer vision pipeline to detect and quantify biofilm aggregates from brightfield microscopy images. BacQuant distinguishes individual cells from aggregates and provides improved cell counts. Automated counts matched manual microscopic counts and exceeded viable plate counts, highlighting shortcomings of conventional methods and offering a scalable, inexpensive alternative for biofilm quantification.
","acknowledgements":"","authors":[{"affiliations":["Binghamton University","Binghamton University"],"departments":["First-year Research Immersion Program","Department of Biological Sciences"],"credit":["conceptualization","dataCuration","formalAnalysis","methodology","writing_originalDraft"],"email":"tevinflom@gmail.com","firstName":"Tevin","lastName":"Flom","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","supervision","writing_reviewEditing"],"email":"uciftci@binghamton.edu","firstName":"Umur A. ","lastName":"Ciftci","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program,"],"credit":["conceptualization","methodology","resources","supervision","writing_reviewEditing"],"email":"clight@binghamton.edu","firstName":"Caitlin J. ","lastName":"Light","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","fundingAcquisition","methodology","project","resources","supervision","writing_originalDraft","writing_reviewEditing","validation"],"email":"wredman@binghamton.edu","firstName":"Whitni K.","lastName":"Redman","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":null,"extendedData":[],"funding":"This work was supported by the First-year Research Immersion Program at Binghamton University, the Binghamton University Scholars Program, and the Harpur’s Edge Award from the External Scholarship and Undergraduate Research Center at Binghamton University.
","image":{"url":"https://portal.micropublication.org/uploads/cac57b9f1e45f86167a507d8299d9773.jpeg"},"imageCaption":"(A) Biofilm growth, sample preparation, and analysis. 48-hour PAO1 biofilms were collected, rinsed to removed unattached cells, and sonicated to liberate biofilm-encased bacteria. The resulting suspension were mounted on slides, stained with safranin, and imaged by brightfield microscopy. Images were analyzed using the BacQuant pipeline to distinguish free cells from aggregates and determine cell counts. In parallel, cell viability was assessed by serial dilution and plating to obtain CFU/mL measurements. (B) Representative images of stained cell populations. Cells were stained with a safranin-based counterstain for one minute in order to show both free cell and aggregate morphologies. Examples of free cells (A) and aggregates (B) are shown by the labeled arrows. These images were typical of the aforementioned experimental design. Representative of n= 32 images. Scale bars represent 10 µm. (C,D) Validation of the Pipeline Against Ground-Truth Labels. Estimation plots of free cells (C) and aggregates (D) are shown. Ground truth counts were obtained by manually counting the number of free cells and aggregates in each image. The ground truth counts across 10 representative images from the total experimental set of 300 images were measured, and the pipeline was run once on each sample image. The green circles represent ground truth counts, and orange circles represent BacQuant measurements. (E,F) Comparison of BacQuant Against Viable Plate Cell Counts. Viable plate counts were determined via serial dilution and drop-plating of the post-sonicated sample. Representative images of drop-plates and corresponding microscopy images (E), along with the CFU/mL calculated from BacQuant (left column) and viable plate count (right column). Average cells/mL counts from BacQuant (orange bar) were compared against CFU/mL counts from viable plate counts (blue) in (F). The estimation plots above were generated from paired t-tests.; ****, p<0.0001, n=10. Statistical significance was determined with an unpaired t-test, and error bars represent standard deviation. ****, p<0.0001, n=18-22 biofilm samples with 10 images per sample.
","imageTitle":"Work Flow and Representative Results of BacQuant
","methods":"Materials and Methods
Bacterial Culture Preparation
P. aeruginosa PAO1 liquid cultures were routinely grown in lysogeny broth (LB) (SigmaAldrich®, Cat# L3022) at 37°C under 220 RPM shaking conditions for 18 hours in 125 mL flasks.
In vitro Well-plate Model
P. aeruginosa biofilms were cultivated in 24-well non-tissue culture-treated plates (VWR, Cat# 10861-558) for 48 hours at 37°C under 80 RPM shaking conditions. Individual wells were inoculated with 106 CFU/mL in 800 µL. After the 48-hour incubation, media was removed, and each well was rinsed with 1 mL of 0.85% saline solution to remove any unattached or lysed cells. This growth protocol was adapted from Fleming et. al (Fleming et al., 2020). The entire plate was covered with parafilm and placed into a water bath sonicator (Fischer Scientific, Cat# 15337411) for 30 minutes. After sonication, the contents of each well were moved to a respective 1.5 mL microcentrifuge tube.
Slide Preparation and Viability Assay
After harvesting, 10 µL of cell solution was added onto a glass microscope slide (Globe Scientific, Inc, Cat# 1304). The solution was spread into a 1 cm2 area at the center of each slide using an inoculation loop. The samples were then heat-fixed onto the slides and stained with Safranin Advanced Counterstain (Hardy Diagnostics, Cat# GK400) for 1 minute and subsequently rinsed with deionized water. The slides were then wet-mounted using immersion oil and topped with a glass cover slip. Concurrently, the remaining cell solution was serially diluted in saline and drop-plated onto solid LB agar and incubated for 18-24 hours at 37°C for CFU enumeration.
Brightfield Microscopy
All slides were imaged using an Olympus BX43 microscope under oil immersion at 100x magnification. Five representative images of each slide were taken, representing one well in the 24-well plate model. An Olympus DP22 camera was used with CellSens Entry software to take the images. The size of each image and light intensity was constant across all images. The dimensions of each image were measured to be 5000 x 7000 nm, in metric units using the Linear Ruler tool in CellSens Entry. Images were saved and label-matched to their corresponding drop-plate.
Image Analysis
A custom computer vision image analysis pipeline was built using Python v3.8 programming language. A stand-alone version of the pipeline is provided and can be run as an online notebook in or as a script with a Python IDE. All necessary interaction with the code is clearly labeled for the user as comments in the code, as this pipeline is not based on a graphical user interface (GUI). This pipeline should be adapted for individual experiments, as it is currently optimized for use with P. aeruginosa mono-species biofilm cells. All parameters are clearly labeled in the code and are easily customizable. All code is freely available, with the implementation of each step of the pipeline clearly shown. The code is available through Google Colaboratory (https://colab.research.google.com/drive/1JWpmjoXKtgYmeux7MUyBH6syZLHm1inf?usp=sharing). We recommend this pipeline be used with GPU acceleration for high-throughput analyses, but running the process in the cloud or with a CPU is also easily supported. The pipeline takes .png file format images as the primary input, as well as user-specified parameters such as the area of an individual cell in pixels and image size.
The first step of the pipeline entails generating a binary mask of the image based on color threshold values. The mask eliminates background noise in the image and standardizes the shape and sizes of all image features. Following masking, a diameter-based size thresholding algorithm is used to label large connected regions in the image as aggregates in blue, and smaller regions as free cells in yellow. This simplifies the visual distinction between aggregates and free cells. From this color-changing step, the percentages of aggregated biomass and free cells are determined via color-based pixel pooling.
Concurrently, a contour detection algorithm from the OpenCV (https://github.com/itseez/opencv) library is used to extract aggregated regions of cells as features. The edges of each aggregate are separated from the background image using Canny edge detection (DOI: 10.1109/TPAMI.1986.4767851). Then, the remaining regions are enclosed by a boundary line through contour detection. This allows for the thorough definition of the 2D shape of each aggregate. From these contours, the area of each aggregate is calculated in pixels and converted to square microns. Cell counts within the aggregates are based on the area of an individual cell in pixels, which is defined by the user at the beginning of the pipeline. It is important to note that this process only works on two-dimensional images. It also can be used with epifluorescence microscopy and live/dead staining with modification in the image processing code to exclude dead cells. It is not optimized for Z-stack images, though it can be modified to work with image arrays and high-throughput imaging results. This makes BacQuant a useful tool for more basic laboratories without complex equipment, software, or imaging capabilities.
The output of this pipeline includes the percent aggregated biomass in each image, number of aggregates, estimated number of cells in aggregates, total estimated cells in each image, and estimated calculation of cells per mL of solution. The raw data from this analysis is stored as a .csv file format spreadsheet. We recommend that further processing of the data be conducted in Microsoft Excel.
Statistical Analysis
Statistical analysis was conducted using GraphPad Prism. A paired t-test was utilized to compare ground truth and BacQuant counts of aggregates and free cells. An unpaired t-test was used to compare viable plate count assay results with BacQuant results. All analyses were conducted to determine statistical significance with an alpha value of 0.05.
","reagents":"","patternDescription":"Biofilms are communities of bacteria encased in a matrix composed of extracellular polymeric substances (EPS) including lipids, proteins, extracellular DNA, and polysaccharides (Flemming et al., 2025). These communities are ubiquitous across medical, industrial, and environmental systems and are responsible for an estimated $4 trillion in global economic damage annually (Camara et al., 2022). Clinically, biofilms contribute to approximately 80% of human infections, where the EPS confers protection from antibiotics and host immune responses (Fedorowski, Moller, & Melander, 2013). Both Gram-positive species, such as Staphylococcus aureus, and Gram-negative species, such as Pseudomonas aeruginosa, exhibit biofilm-associated tolerance and persistence that complicate treatment (Schaber et al., 2007; Usui, Yoshii, Thiriet-Rupert, Ghigo, & Beloin, 2023).
A major challenge in biofilm research is accurate quantification of bacterial populations following experimental treatment. Conventional colony-forming unit (CFU) or viable plate counting (VPC) methods significantly underestimate biofilm cell numbers because aggregates and individual cells both yield a single colony on agar plates (Beal et al., 2020; Martini, Boddu, Nemenman, & Vega, 2024). Mechanical disruption methods, such as homogenization or sonication, or commonly used to break up biofilms prior to plating, but these approaches do not fully dissociate EPS-bound aggregates and therefore still produce inaccurate counts (Buckingham-Meyer et al., 2022).
Recent advances in computer vision and microscopy offer alternative strategies for microbial quantification. Image-based methods can distinguish individual cells from aggregates using segmentation and contour detection, providing structural information that is lost in culture-based approaches (Holicheva et al., 2025; J. Wang et al., 2022). However, existing tools such as BiofilmQ and confocal-based pipelines are computationally intensive, require specialized hardware, or are not designed for post-disruption enumeration (Hartmann et al., 2021) (Mountcastle et al., 2021).
Here, we present BacQuant, a computationally inexpensive image processing pipeline to improve quantification of biofilm cells following sonication using brightfield microscopy. Using P. aeruginosa as a model organism, BacQuant differentiates free cells from aggregates and estimates aggregate cell numbers to improve quantification relative to VPC methods (Figure 1A). It is important to note that BacQuant does not intend to count viable cells. Rather, it estimates the total cell count in the biofilm sample including both live and dead cells. It does not estimate live cells like traditional VPC methods do.
Definition of Free Cells and Aggregates
Sonication of P. aeruginosa biofilms consistently yields two morphologies – free individual cells and EPS-encased aggregates (Figure 1B). Free cells displayed relatively uniform size and shape, whereas aggregates varied widely in morphology, often appearing circular with irregular edges and measuring up to ~1000 µm in diameter. Safranin staining revealed higher color saturation in aggregates, reflecting increased biomass and EPS. Both morphologies were present in all samples, indicating incomplete disaggregation by sonication.
BacQuant Aggregate Counts Do Not Differ From Ground Truth
To evaluate BacQuant performance, images were treated as biological replicates and manually annotated to generate ground truth labels. There was a significant difference (p=0.0001; Figure 1C) in the number of free cells identified by ground truth and those identified by BacQuant. This underestimation likely arises from pixel-based normalization to a user-defined average cell area, which does not account for cell orientation or morphological variability.
In contrast, BacQuant slightly overestimated aggregate counts compared to ground truth, although this difference was not statistically significant (p=0.0569; Figure 1D). This trend likely reflects conservative edge detection during segmentation and the lack of depth information in two-dimensional images, which may obscure internal aggregate structure.
BacQuant Enumerates More Cells Than Viable Plate Counts
BacQuant total cell counts were compared to traditional VPC measurements obtained via serial dilution and drop-plating (Figure 1A,E). Across twenty samples, BacQuant produced significantly higher mean cell counts than VPC (p=0.0001), with averages of 1.18 x 1011 and 4.46 x 107 cells, respectively (Figure 1F). BacQuant results were more tightly clustered, whereas VPC values exhibited greater variability.
This discrepancy is consistent with the presence of biofilm aggregates that contain many cells but yield only a single colony on agar plates. Additionally, BacQuant includes dead cells in its estimates, whereas VPC counts only viable cells, further contributing to higher total counts.
This study introduces BacQuant, a computationally inexpensive image-processing pipeline for quantifying biofilm cells following sonication. BacQuant addresses a fundamental limitation of CFU-based methods by explicitly accounting for residual aggregates that persist after mechanical disruption and confound viable cell estimates (Fleming et al., 2020). By combining thresholding with edge and contour detection, BacQuant enables reproducible instant segmentation of individual cells and aggregates.
Sonication was selected as the representative disruption method due to its widespread use in laboratory, clinical, and industrial contexts. While sonication effectively detaches biofilms from surfaces, it does not reliably dissociate biofilms into single cells, leaving EPS-bound aggregates intact (Kragh et al., 2016) (Figure 1B). These aggregates are indistinguishable from single cells in VPC assays, leading to systematic underestimation of bacterial load. BacQuant circumvents this issue by estimating the number of cells within aggregates, producing significantly higher and more consistent counts than VPC (Figure 1F).
A major strength of BacQuant is its accessibility. Unlike machine learning-based or confocal approaches, the pipeline does not require training data, specialized hardware, or bulky software installations. It can be run on standard CPUs and adapted to diverse imaging conditions, making it broadly applicable across laboratories. The method captures biologically meaningful structure features, specifically the distinction between free cells and aggregates, that are routinely missed by culture-based techniques.
Nevertheless, BacQuant has limitations. In dense images, over-segmentation may occur, leading to slight overestimation of aggregate numbers (Figure 1D). Conversely, free cells are underestimated due to reliance on an average cell area parameter that does not account for morphological variability (Figure 1C). The pipeline also cannot distinguish between live and dead cells, which may inflate estimates relative to viable counts. Additionally, accurate performance depends on high-contrast microscopy images and correct calibration for each experimental setup.
Despite these constraints, BacQuant provides a rapid and scalable complement to traditional microbiological methods. The entire analysis can be completed within minutes, compared to hours or days required for plating assays. Accurate biofilm quantification is critical for antimicrobial testing, clinical research, and environmental monitoring, and BacQuant offers a practical tool for improving structural enumeration of bacterial populations (Folliero et al., 2021).
Future work should integrate live/dead staining, molecular viability markers, and three-dimension imaging to refine estimates of active biofilm populations (Sauer et al., 2022; Wang, Zhu, Zheng, Dong, & Liu, 2022). Incorporation of machine learning-based segmentation could further enhance classification of complex morphologies. Ultimately, BacQuant provides a foundation for automated, image-based biofilm quantification and highlights the value of computer vision approaches in microbiology research..
","references":[{"reference":"Acosta N, Waddell B, Heirali A, Somayaji R, Surette MG, Workentine ML, Rabin HR, Parkins MD. 2020. Cystic Fibrosis Patients Infected With Epidemic Pseudomonas aeruginosa Strains Have Unique Microbial Communities. Frontiers in Cellular and Infection Microbiology 10: 10.3389/fcimb.2020.00173.
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","pubmedId":"","doi":"10.1128/mSphere.00111-15"},{"reference":"Hartmann R, Jeckel H, Jelli E, Singh PK, Vaidya S, Bayer M, et al., Drescher. 2021. Quantitative image analysis of microbial communities with BiofilmQ. Nature Microbiology 6: 151-156.
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","pubmedId":"","doi":"10.1146/annurev.micro.56.012302.160705"},{"reference":"Usui M, Yoshii Y, Thiriet-Rupert S, Ghigo JM, Beloin C. 2023. Intermittent antibiotic treatment of bacterial biofilms favors the rapid evolution of resistance. Communications Biology 6: 10.1038/s42003-023-04601-y.
","pubmedId":"","doi":"10.1038/s42003-023-04601-y"},{"reference":"Wang J, Tabassum N, Toma TT, Wang Y, Gahlmann A, Acton ST. 2022. 3D GAN image synthesis and dataset quality assessment for bacterial biofilm. Bioinformatics 38: 4598-4604.
","pubmedId":"","doi":"10.1093/bioinformatics/btac529"},{"reference":"Wang S, Zhu H, Zheng G, Dong F, Liu C. 2022. Dynamic Changes in Biofilm Structures under Dynamic Flow Conditions. Applied and Environmental Microbiology 88: 10.1128/aem.01072-22.
","pubmedId":"","doi":"10.1128/aem.01072-22"}],"title":"BacQuant: A Scalable Automated Image Processing Pipeline for Quantifying Biofilm Aggregates
","reviews":[{"reviewer":{"displayName":"Ilana Kolodkin Gal"},"openAcknowledgement":true,"status":{"submitted":true}}],"curatorReviews":[]},{"id":"1d267bd1-e0c7-4afe-9074-711e2a617232","decision":"edit","abstract":"Traditional microbiology methods rely on viable plate counting to quantify bacterial populations but often underestimate biofilm cell density because matrix-encased aggregates can produce a single colony despite containing many cells. Here, we present BacQuant, a computer vision pipeline developed in Python and OpenCV to quantify biofilm aggregates from brightfield microscopy images. Using image thresholding, segmentation, and contour detection, BacQuant distinguishes individual cells from aggregates and estimates total cell burden more comprehensively than viable plate counting alone. Automated counts closely matched manual microscopy counts while producing higher estimated densities, highlighting BacQuant as a scalable, inexpensive complimentary method for biofilm quantification.
","acknowledgements":"","authors":[{"affiliations":["Binghamton University","Binghamton University"],"departments":["First-year Research Immersion Program","Department of Biological Sciences"],"credit":["conceptualization","dataCuration","formalAnalysis","methodology","writing_originalDraft"],"email":"tevinflom@gmail.com","firstName":"Tevin","lastName":"Flom","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","supervision","writing_reviewEditing"],"email":"uciftci@binghamton.edu","firstName":"Umur A. ","lastName":"Ciftci","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program,"],"credit":["conceptualization","methodology","resources","supervision","writing_reviewEditing"],"email":"clight@binghamton.edu","firstName":"Caitlin J. ","lastName":"Light","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","fundingAcquisition","methodology","project","resources","supervision","writing_originalDraft","writing_reviewEditing","validation"],"email":"wredman@binghamton.edu","firstName":"Whitni K.","lastName":"Redman","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":null},"extendedData":[],"funding":"This work was supported by the First-year Research Immersion Program at Binghamton University, the Binghamton University Scholars Program, and the Harpur’s Edge Award from the External Scholarship and Undergraduate Research Center at Binghamton University.
","image":{"url":"https://portal.micropublication.org/uploads/cac57b9f1e45f86167a507d8299d9773.jpeg"},"imageCaption":"(A) Biofilm growth, sample preparation, and analysis. 48-hour PAO1 biofilms were collected, rinsed to removed unattached cells, and sonicated to liberate biofilm-encased bacteria. The resulting suspension were mounted on slides, stained with safranin, and imaged by brightfield microscopy. Images were analyzed using the BacQuant pipeline to distinguish free cells from aggregates and determine cell counts. In parallel, cell viability was assessed by serial dilution and plating to obtain CFU/mL measurements. (B) Representative images of stained cell populations. Cells were stained with a safranin-based counterstain for one minute in order to show both free cell and aggregate morphologies. Examples of free cells (A) and aggregates (B) are shown by the labeled arrows. These images were typical of the aforementioned experimental design. Representative of n= 32 images. Scale bars represent 10 µm. (C,D) Validation of the Pipeline Against Ground-Truth Labels. Estimation plots of free cells (C) and aggregates (D) are shown. Ground truth counts were obtained by manually counting the number of free cells and aggregates in each image. The ground truth counts across 10 representative images from the total experimental set of 300 images were measured, and the pipeline was run once on each sample image. The green circles represent ground truth counts, and orange circles represent BacQuant measurements. (E,F) Comparison of BacQuant Against Viable Plate Cell Counts. Viable plate counts were determined via serial dilution and drop-plating of the post-sonicated sample. Representative images of drop-plates and corresponding microscopy images (E), along with the CFU/mL calculated from BacQuant (left column) and viable plate count (right column). Average cells/mL counts from BacQuant (orange bar) were compared against CFU/mL counts from viable plate counts (blue) in (F). The estimation plots above were generated from paired t-tests.; ****, p<0.0001, n=10. Statistical significance was determined with an unpaired t-test, and error bars represent standard deviation. ****, p<0.0001, n=18-22 biofilm samples with 10 images per sample.
","imageTitle":"Work Flow and Representative Results of BacQuant
","methods":"Materials and Methods
Bacterial Culture Preparation
P. aeruginosa PAO1 liquid cultures were routinely grown in lysogeny broth (LB) (SigmaAldrich®, Cat# L3022) at 37°C under 220 RPM shaking conditions for 18 hours in 125 mL flasks.
In vitro Well-plate Model
P. aeruginosa biofilms were cultivated in 24-well non-tissue culture-treated plates (VWR, Cat# 10861-558) for 48 hours at 37°C under 80 RPM shaking conditions. Individual wells were inoculated with 106 CFU/mL in 800 µL. After the 48-hour incubation, media was removed, and each well was rinsed with 1 mL of 0.85% saline solution to remove any unattached or lysed cells. This growth protocol was adapted from Fleming et. al (Fleming et al., 2020). The entire plate was covered with parafilm and placed into a water bath sonicator (Fischer Scientific, Cat# 15337411) for 30 minutes. This sonication condition was selected based on previously published biofilm disruption protocols (Fleming, Chahin, & Rumbaugh, 2017; Redman et al., 2021) and was not used independently optimized in the present study. Because the purpose of this work was to evaluate BacQuant as an image-based method for quantifying cells and residual aggregates following a standard disruption step, additional optimization of sonication intensity, during, or enzymatic pretreatment was outside the scope of this study. After sonication, the contents of each well were moved to a respective 1.5 mL microcentrifuge tube.
Slide Preparation and Viability Assay
After harvesting, 10 µL of cell solution was added onto a glass microscope slide (Globe Scientific, Inc, Cat# 1304). The solution was spread into a 1 cm2 area at the center of each slide using an inoculation loop. The samples were then heat-fixed onto the slides and stained with Safranin Advanced Counterstain (Hardy Diagnostics, Cat# GK400) for 1 minute and subsequently rinsed with deionized water. The slides were then wet-mounted using immersion oil and topped with a glass cover slip. Concurrently, the remaining cell solution was serially diluted in saline and drop-plated onto solid LB agar and incubated for 18-24 hours at 37°C for CFU enumeration.
Brightfield Microscopy
All slides were imaged using an Olympus BX43 microscope under oil immersion at 100x magnification. Five representative images of each slide were taken, representing one well in the 24-well plate model. An Olympus DP22 camera was used with CellSens Entry software to take the images. The size of each image and light intensity was constant across all images. The dimensions of each image were measured to be 5000 x 7000 nm, in metric units using the Linear Ruler tool in CellSens Entry. Images were saved and label-matched to their corresponding drop-plate.
Image Analysis
A custom computer vision image analysis pipeline was built using Python v3.8 programming language. A stand-alone version of the pipeline is provided and can be run as an online notebook in or as a script with a Python IDE. All necessary interaction with the code is clearly labeled for the user as comments in the code, as this pipeline is not based on a graphical user interface (GUI). This pipeline should be adapted for individual experiments, as it is currently optimized for use with P. aeruginosa mono-species biofilm cells. All parameters are clearly labeled in the code and are easily customizable. All code is freely available, with the implementation of each step of the pipeline clearly shown. The code is available through Google Colaboratory (https://colab.research.google.com/drive/1JWpmjoXKtgYmeux7MUyBH6syZLHm1inf?usp=sharing). We recommend this pipeline be used with GPU acceleration for high-throughput analyses, but running the process in the cloud or with a CPU is also easily supported. The pipeline takes .png file format images as the primary input, as well as user-specified parameters such as the area of an individual cell in pixels and image size.
The first step of the pipeline entails generating a binary mask of the image based on color threshold values. The mask eliminates background noise in the image and standardizes the shape and sizes of all image features. Following masking, a diameter-based size thresholding algorithm is used to label large connected regions in the image as aggregates in blue, and smaller regions as free cells in yellow. This simplifies the visual distinction between aggregates and free cells. From this color-changing step, the percentages of aggregated biomass and free cells are determined via color-based pixel pooling. For the purpose of this analysis, an aggregate is defined as any connected segmented region with an estimated diameter greater than the user-defined threshold for a single cell. Segmented regions at or below this threshold are classified as free cells. Smaller clusters like doublets, triplets, and quadruplets are classified according to the same size-based rules and were not assigned separate biological categories. If the connected region exceeds the aggregate threshold, it is labeled as an aggregate and its cell number is estimated by dividing the segmented area by the user defined single-cell average area in pixels. If the region does not exceed the threshold, it is classified as a free-cell region.
Concurrently, a contour detection algorithm from the OpenCV (https://github.com/itseez/opencv) library is used to extract aggregated regions of cells as features. The edges of each aggregate are separated from the background image using Canny edge detection (DOI: 10.1109/TPAMI.1986.4767851). Then, the remaining regions are enclosed by a boundary line through contour detection. This allows for the thorough definition of the 2D shape of each aggregate. From these contours, the area of each aggregate is calculated in pixels and converted to square microns. Cell counts within the aggregates are based on the area of an individual cell in pixels, which is defined by the user at the beginning of the pipeline. It is important to note that this process only works on two-dimensional images. It also can be used with epifluorescence microscopy and live/dead staining with modification in the image processing code to exclude dead cells. It is not optimized for Z-stack images, though it can be modified to work with image arrays and high-throughput imaging results. This makes BacQuant a useful tool for more basic laboratories without complex equipment, software, or imaging capabilities.
The output of this pipeline includes the percent aggregated biomass in each image, number of aggregates, estimated number of cells in aggregates, total estimated cells in each image, and estimated calculation of cells per mL of solution. The raw data from this analysis is stored as a .csv file format spreadsheet. We recommend that further processing of the data be conducted in Microsoft Excel.
Statistical Analysis
Statistical analysis was conducted using GraphPad Prism. A paired t-test was utilized to compare ground truth and BacQuant counts of aggregates and free cells. An unpaired t-test was used to compare viable plate count assay results with BacQuant results. All analyses were conducted to determine statistical significance with an alpha value of 0.05.
","reagents":"","patternDescription":"Biofilms are communities of bacteria encased in a matrix composed of extracellular polymeric substances (EPS) including lipids, proteins, extracellular DNA, and polysaccharides (Flemming et al., 2025). These communities are ubiquitous across medical, industrial, and environmental systems and are responsible for an estimated $4 trillion in global economic damage annually (Camara et al., 2022). Clinically, biofilms contribute to approximately 80% of human infections, where the EPS confers protection from antibiotics and host immune responses (Fedorowski, Moller, & Melander, 2013). Both Gram-positive species, such as Staphylococcus aureus, and Gram-negative species, such as Pseudomonas aeruginosa, exhibit biofilm-associated tolerance and persistence that complicate treatment (Schaber et al., 2007; Usui, Yoshii, Thiriet-Rupert, Ghigo, & Beloin, 2023).
A major challenge in biofilm research is accurate quantification of bacterial populations following experimental treatment. Conventional colony-forming unit (CFU) or viable plate counting (VPC) methods remain widely used because they specifically measure culturable, viable bacteria. However, in biofilm samples, VPC may not fully reflect the total number of cells present because aggregates containing multiple cells can produce a single colony if they are not completely freed from the matrix before plating (Beal et al., 2020; Martini, Boddu, Nemenman, & Vega, 2024). Mechanical disruption methods, such as homogenization or sonication, are commonly used to break up biofilms prior to plating. While optimized detachment and sonication protocols can substantially improve biofilm separation, incomplete dissociation of EPS-bound aggregates may still occur depending on the organism, biofilm structure, treatments, and disruption protocol used (Buckingham-Meyer et al., 2022; Korshoj & Kielian, 2024).
Recent advances in computer vision and microscopy offer alternative strategies for microbial quantification. Image-based methods can distinguish individual cells from aggregates using segmentation and contour detection, providing structural information that is lost in culture-based approaches (Holicheva et al., 2025; J. Wang et al., 2022). However, existing tools such as BiofilmQ and confocal-based pipelines are computationally intensive, require specialized hardware, or are not designed for post-disruption enumeration (Hartmann et al., 2021; Mountcastle et al., 2021).
Here, we present BacQuant, a computationally inexpensive image processing pipeline to improve quantification of biofilm cells following sonication using brightfield microscopy. Using P. aeruginosa as a model organism, BacQuant differentiates free cells from aggregates and estimates aggregate cell numbers to improve quantification relative to VPC methods (Figure 1A). It is important to note that BacQuant does not intend to count viable cells. Rather, it estimates the total cell count in the biofilm sample including both live and dead cells. It does not estimate live cells like traditional VPC methods do.
Definition of Free Cells and Aggregates
Sonication of P. aeruginosa biofilms consistently yields two morphologies – free individual cells and EPS-encased aggregates (Figure 1B). Operationally, BacQuant defines these categories by segmented object size rather than by visual interpretation alone. Free cells displayed relatively uniform size and shape, whereas aggregates varied widely in morphology, often appearing circular with irregular edges and measuring up to ~1000 µm in diameter. Safranin staining revealed higher color saturation in aggregates, reflecting increased biomass and EPS. Both morphologies were present in all samples, indicating incomplete disaggregation by sonication.
BacQuant Aggregate Counts Do Not Differ From Ground Truth
To evaluate BacQuant performance, images were treated as biological replicates and manually annotated to generate ground truth labels. There was a significant difference (p=0.0001; Figure 1C) in the number of free cells identified by ground truth and those identified by BacQuant. This underestimation likely arises from pixel-based normalization to a user-defined average cell area, which does not account for cell orientation or morphological variability.
In contrast, BacQuant slightly overestimated aggregate counts compared to ground truth, although this difference was not statistically significant (p=0.0569; Figure 1D). This trend likely reflects conservative edge detection during segmentation and the lack of depth information in two-dimensional images, which may obscure internal aggregate structure.
BacQuant Enumerates More Cells Than Viable Plate Counts
BacQuant total cell counts were compared to traditional VPC measurements obtained via serial dilution and drop-plating (Figure 1A,E). Across twenty samples, BacQuant produced significantly higher mean cell counts than VPC (p=0.0001), with averages of 1.18 x 1011 and 4.46 x 107 cells, respectively (Figure 1F). BacQuant results were more tightly clustered, whereas VPC values exhibited greater variability.
This discrepancy is consistent with the presence of biofilm aggregates that contain many cells but yield only a single colony on agar plates. Additionally, BacQuant includes dead cells in its estimates, whereas VPC counts only viable cells, further contributing to higher total counts.
Discussion/Conclusion
This study introduces BacQuant, a computationally inexpensive image-processing pipeline for quantifying biofilm cells following sonication. BacQuant addresses a fundamental limitation of CFU-based methods by explicitly accounting for residual aggregates that persist after mechanical disruption and confound viable cell estimates (Fleming et al., 2020). By combining thresholding with edge and contour detection, BacQuant enables reproducible instant segmentation of individual cells and aggregates.
Sonication was selected as the representative disruption method due to its widespread use in laboratory, clinical, and industrial contexts. While sonication effectively detaches biofilms from surfaces, it does not reliably dissociate biofilms into single cells, leaving EPS-bound aggregates intact (Kragh et al., 2016) (Figure 1B). These aggregates are indistinguishable from single cells in VPC assays, leading to systematic underestimation of bacterial load. BacQuant circumvents this issue by estimating the number of cells within aggregates, producing significantly higher and more consistent counts than VPC (Figure 1F).
A major strength of BacQuant is its accessibility. Unlike machine learning-based or confocal approaches, the pipeline does not require training data, specialized hardware, or bulky software installations. It can be run on standard CPUs and adapted to diverse imaging conditions, making it broadly applicable across laboratories. The method captures biologically meaningful structure features, specifically the distinction between free cells and aggregates, that are routinely missed by culture-based techniques.
Nevertheless, BacQuant has limitations. In dense images, over-segmentation may occur, leading to slight overestimation of aggregate numbers (Figure 1D). Conversely, free cells are underestimated due to reliance on an average cell area parameter that does not account for morphological variability (Figure 1C). The pipeline also cannot distinguish between live and dead cells, which may inflate estimates relative to viable counts. If cells occur in small clusters like doublets or triplets, it is processed as an aggregate while they could be a few individual cells in close promixity to one another. Another major limitation is the imposition of 2D structure on the 3D aggregate morphology. Because BacQuant only uses two-dimensional estimation, edge detection is conservative and does not take the height of the structure into account when determining cell density within large cell clusters, dampening the true estimate of cell density within. Additionally, accurate performance depends on high-contrast microscopy images and correct calibration for each experimental setup.
Despite these constraints, BacQuant provides a rapid and scalable complement to traditional microbiological methods. The entire analysis can be completed within minutes, compared to hours or days required for plating assays. Accurate biofilm quantification is critical for antimicrobial testing, clinical research, and environmental monitoring, and BacQuant offers a practical tool for improving structural enumeration of bacterial populations (Folliero et al., 2021).
Future work should integrate live/dead staining, molecular viability markers, and three-dimension imaging to refine estimates of active biofilm populations (Sauer et al., 2022; Wang, Zhu, Zheng, Dong, & Liu, 2022). Incorporation of machine learning-based segmentation could further enhance classification of complex morphologies. Ultimately, BacQuant provides a foundation for automated, image-based biofilm quantification and highlights the value of computer vision approaches in microbiology research..
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","pubmedId":"34825176","doi":"10.1016/j.bioflm.2021.100061 "}],"title":"BacQuant: A Scalable Automated Image Processing Pipeline for Quantifying Biofilm Aggregates
","reviews":[],"curatorReviews":[]},{"id":"7a5f9f53-c034-4167-bb59-a28de8c6c643","decision":"accept","abstract":"Traditional microbiology methods rely on viable plate counting to quantify bacterial populations but often underestimate biofilm cell density because matrix-encased aggregates can produce a single colony despite containing many cells. Here, we present BacQuant, a computer vision pipeline developed in Python and OpenCV to quantify biofilm aggregates from brightfield microscopy images. Using image thresholding, segmentation, and contour detection, BacQuant distinguishes individual cells from aggregates and estimates total cell burden more comprehensively than viable plate counting alone. Automated counts closely matched manual microscopy counts while producing higher estimated densities, highlighting BacQuant as a scalable, inexpensive complimentary method for biofilm quantification.
","acknowledgements":"","authors":[{"affiliations":["Binghamton University","Binghamton University"],"departments":["First-year Research Immersion Program","Department of Biological Sciences"],"credit":["conceptualization","dataCuration","formalAnalysis","methodology","writing_originalDraft"],"email":"tevinflom@gmail.com","firstName":"Tevin","lastName":"Flom","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","supervision","writing_reviewEditing"],"email":"uciftci@binghamton.edu","firstName":"Umur A. ","lastName":"Ciftci","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program,"],"credit":["conceptualization","methodology","resources","supervision","writing_reviewEditing"],"email":"clight@binghamton.edu","firstName":"Caitlin J. ","lastName":"Light","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","fundingAcquisition","methodology","project","resources","supervision","writing_originalDraft","writing_reviewEditing","validation"],"email":"wredman@binghamton.edu","firstName":"Whitni K.","lastName":"Redman","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":null},"extendedData":[],"funding":"This work was supported by the First-year Research Immersion Program at Binghamton University, the Binghamton University Scholars Program, and the Harpur’s Edge Award from the External Scholarship and Undergraduate Research Center at Binghamton University.
","image":{"url":"https://portal.micropublication.org/uploads/cac57b9f1e45f86167a507d8299d9773.jpeg"},"imageCaption":"(A) Biofilm growth, sample preparation, and analysis. 48-hour PAO1 biofilms were collected, rinsed to removed unattached cells, and sonicated to liberate biofilm-encased bacteria. The resulting suspension were mounted on slides, stained with safranin, and imaged by brightfield microscopy. Images were analyzed using the BacQuant pipeline to distinguish free cells from aggregates and determine cell counts. In parallel, cell viability was assessed by serial dilution and plating to obtain CFU/mL measurements. (B) Representative images of stained cell populations. Cells were stained with a safranin-based counterstain for one minute in order to show both free cell and aggregate morphologies. Examples of free cells (A) and aggregates (B) are shown by the labeled arrows. These images were typical of the aforementioned experimental design. Representative of n= 32 images. Scale bars represent 10 µm. (C,D) Validation of the Pipeline Against Ground-Truth Labels. Estimation plots of free cells (C) and aggregates (D) are shown. Ground truth counts were obtained by manually counting the number of free cells and aggregates in each image. The ground truth counts across 10 representative images from the total experimental set of 300 images were measured, and the pipeline was run once on each sample image. The green circles represent ground truth counts, and orange circles represent BacQuant measurements. (E,F) Comparison of BacQuant Against Viable Plate Cell Counts. Viable plate counts were determined via serial dilution and drop-plating of the post-sonicated sample. Representative images of drop-plates and corresponding microscopy images (E), along with the CFU/mL calculated from BacQuant (left column) and viable plate count (right column). Average cells/mL counts from BacQuant (orange bar) were compared against CFU/mL counts from viable plate counts (blue) in (F). The estimation plots above were generated from paired t-tests.; ****, p<0.0001, n=10. Statistical significance was determined with an unpaired t-test, and error bars represent standard deviation. ****, p<0.0001, n=18-22 biofilm samples with 10 images per sample.
","imageTitle":"Work Flow and Representative Results of BacQuant
","methods":"Materials and Methods
Bacterial Culture Preparation
P. aeruginosa PAO1 liquid cultures were routinely grown in lysogeny broth (LB) (SigmaAldrich®, Cat# L3022) at 37°C under 220 RPM shaking conditions for 18 hours in 125 mL flasks.
In vitro Well-plate Model
P. aeruginosa biofilms were cultivated in 24-well non-tissue culture-treated plates (VWR, Cat# 10861-558) for 48 hours at 37°C under 80 RPM shaking conditions. Individual wells were inoculated with 106 CFU/mL in 800 µL. After the 48-hour incubation, media was removed, and each well was rinsed with 1 mL of 0.85% saline solution to remove any unattached or lysed cells. This growth protocol was adapted from Fleming et. al (Fleming et al., 2020). The entire plate was covered with parafilm and placed into a water bath sonicator (Fischer Scientific, Cat# 15337411) for 30 minutes. This sonication condition was selected based on previously published biofilm disruption protocols (Fleming, Chahin, & Rumbaugh, 2017; Redman et al., 2021) and was not used independently optimized in the present study. Because the purpose of this work was to evaluate BacQuant as an image-based method for quantifying cells and residual aggregates following a standard disruption step, additional optimization of sonication intensity, during, or enzymatic pretreatment was outside the scope of this study. After sonication, the contents of each well were moved to a respective 1.5 mL microcentrifuge tube.
Slide Preparation and Viability Assay
After harvesting, 10 µL of cell solution was added onto a glass microscope slide (Globe Scientific, Inc, Cat# 1304). The solution was spread into a 1 cm2 area at the center of each slide using an inoculation loop. The samples were then heat-fixed onto the slides and stained with Safranin Advanced Counterstain (Hardy Diagnostics, Cat# GK400) for 1 minute and subsequently rinsed with deionized water. The slides were then wet-mounted using immersion oil and topped with a glass cover slip. Concurrently, the remaining cell solution was serially diluted in saline and drop-plated onto solid LB agar and incubated for 18-24 hours at 37°C for CFU enumeration.
Brightfield Microscopy
All slides were imaged using an Olympus BX43 microscope under oil immersion at 100x magnification. Five representative images of each slide were taken, representing one well in the 24-well plate model. An Olympus DP22 camera was used with CellSens Entry software to take the images. The size of each image and light intensity was constant across all images. The dimensions of each image were measured to be 5000 x 7000 nm, in metric units using the Linear Ruler tool in CellSens Entry. Images were saved and label-matched to their corresponding drop-plate.
Image Analysis
A custom computer vision image analysis pipeline was built using Python v3.8 programming language. A stand-alone version of the pipeline is provided and can be run as an online notebook in or as a script with a Python IDE. All necessary interaction with the code is clearly labeled for the user as comments in the code, as this pipeline is not based on a graphical user interface (GUI). This pipeline should be adapted for individual experiments, as it is currently optimized for use with P. aeruginosa mono-species biofilm cells. All parameters are clearly labeled in the code and are easily customizable. All code is freely available, with the implementation of each step of the pipeline clearly shown. The code is available through Google Colaboratory (https://colab.research.google.com/drive/1JWpmjoXKtgYmeux7MUyBH6syZLHm1inf?usp=sharing). We recommend this pipeline be used with GPU acceleration for high-throughput analyses, but running the process in the cloud or with a CPU is also easily supported. The pipeline takes .png file format images as the primary input, as well as user-specified parameters such as the area of an individual cell in pixels and image size.
The first step of the pipeline entails generating a binary mask of the image based on color threshold values. The mask eliminates background noise in the image and standardizes the shape and sizes of all image features. Following masking, a diameter-based size thresholding algorithm is used to label large connected regions in the image as aggregates in blue, and smaller regions as free cells in yellow. This simplifies the visual distinction between aggregates and free cells. From this color-changing step, the percentages of aggregated biomass and free cells are determined via color-based pixel pooling. For the purpose of this analysis, an aggregate is defined as any connected segmented region with an estimated diameter greater than the user-defined threshold for a single cell. Segmented regions at or below this threshold are classified as free cells. Smaller clusters like doublets, triplets, and quadruplets are classified according to the same size-based rules and were not assigned separate biological categories. If the connected region exceeds the aggregate threshold, it is labeled as an aggregate and its cell number is estimated by dividing the segmented area by the user defined single-cell average area in pixels. If the region does not exceed the threshold, it is classified as a free-cell region.
Concurrently, a contour detection algorithm from the OpenCV (https://github.com/itseez/opencv) library is used to extract aggregated regions of cells as features. The edges of each aggregate are separated from the background image using Canny edge detection (DOI: 10.1109/TPAMI.1986.4767851). Then, the remaining regions are enclosed by a boundary line through contour detection. This allows for the thorough definition of the 2D shape of each aggregate. From these contours, the area of each aggregate is calculated in pixels and converted to square microns. Cell counts within the aggregates are based on the area of an individual cell in pixels, which is defined by the user at the beginning of the pipeline. It is important to note that this process only works on two-dimensional images. It also can be used with epifluorescence microscopy and live/dead staining with modification in the image processing code to exclude dead cells. It is not optimized for Z-stack images, though it can be modified to work with image arrays and high-throughput imaging results. This makes BacQuant a useful tool for more basic laboratories without complex equipment, software, or imaging capabilities.
The output of this pipeline includes the percent aggregated biomass in each image, number of aggregates, estimated number of cells in aggregates, total estimated cells in each image, and estimated calculation of cells per mL of solution. The raw data from this analysis is stored as a .csv file format spreadsheet. We recommend that further processing of the data be conducted in Microsoft Excel.
Statistical Analysis
Statistical analysis was conducted using GraphPad Prism. A paired t-test was utilized to compare ground truth and BacQuant counts of aggregates and free cells. An unpaired t-test was used to compare viable plate count assay results with BacQuant results. All analyses were conducted to determine statistical significance with an alpha value of 0.05.
","reagents":"","patternDescription":"Biofilms are communities of bacteria encased in a matrix composed of extracellular polymeric substances (EPS) including lipids, proteins, extracellular DNA, and polysaccharides (Flemming et al., 2025). These communities are ubiquitous across medical, industrial, and environmental systems and are responsible for an estimated $4 trillion in global economic damage annually (Camara et al., 2022). Clinically, biofilms contribute to approximately 80% of human infections, where the EPS confers protection from antibiotics and host immune responses (Fedorowski, Moller, & Melander, 2013). Both Gram-positive species, such as Staphylococcus aureus, and Gram-negative species, such as Pseudomonas aeruginosa, exhibit biofilm-associated tolerance and persistence that complicate treatment (Schaber et al., 2007; Usui, Yoshii, Thiriet-Rupert, Ghigo, & Beloin, 2023).
A major challenge in biofilm research is accurate quantification of bacterial populations following experimental treatment. Conventional colony-forming unit (CFU) or viable plate counting (VPC) methods remain widely used because they specifically measure culturable, viable bacteria. However, in biofilm samples, VPC may not fully reflect the total number of cells present because aggregates containing multiple cells can produce a single colony if they are not completely freed from the matrix before plating (Beal et al., 2020; Martini, Boddu, Nemenman, & Vega, 2024). Mechanical disruption methods, such as homogenization or sonication, are commonly used to break up biofilms prior to plating. While optimized detachment and sonication protocols can substantially improve biofilm separation, incomplete dissociation of EPS-bound aggregates may still occur depending on the organism, biofilm structure, treatments, and disruption protocol used (Buckingham-Meyer et al., 2022; Korshoj & Kielian, 2024).
Recent advances in computer vision and microscopy offer alternative strategies for microbial quantification. Image-based methods can distinguish individual cells from aggregates using segmentation and contour detection, providing structural information that is lost in culture-based approaches (Holicheva et al., 2025; J. Wang et al., 2022). However, existing tools such as BiofilmQ and confocal-based pipelines are computationally intensive, require specialized hardware, or are not designed for post-disruption enumeration (Hartmann et al., 2021; Mountcastle et al., 2021).
Here, we present BacQuant, a computationally inexpensive image processing pipeline to improve quantification of biofilm cells following sonication using brightfield microscopy. Using P. aeruginosa as a model organism, BacQuant differentiates free cells from aggregates and estimates aggregate cell numbers to improve quantification relative to VPC methods (Figure 1A). It is important to note that BacQuant does not intend to count viable cells. Rather, it estimates the total cell count in the biofilm sample including both live and dead cells. It does not estimate live cells like traditional VPC methods do.
Definition of Free Cells and Aggregates
Sonication of P. aeruginosa biofilms consistently yields two morphologies – free individual cells and EPS-encased aggregates (Figure 1B). Operationally, BacQuant defines these categories by segmented object size rather than by visual interpretation alone. Free cells displayed relatively uniform size and shape, whereas aggregates varied widely in morphology, often appearing circular with irregular edges and measuring up to ~1000 µm in diameter. Safranin staining revealed higher color saturation in aggregates, reflecting increased biomass and EPS. Both morphologies were present in all samples, indicating incomplete disaggregation by sonication.
BacQuant Aggregate Counts Do Not Differ From Ground Truth
To evaluate BacQuant performance, images were treated as biological replicates and manually annotated to generate ground truth labels. There was a significant difference (p=0.0001; Figure 1C) in the number of free cells identified by ground truth and those identified by BacQuant. This underestimation likely arises from pixel-based normalization to a user-defined average cell area, which does not account for cell orientation or morphological variability.
In contrast, BacQuant slightly overestimated aggregate counts compared to ground truth, although this difference was not statistically significant (p=0.0569; Figure 1D). This trend likely reflects conservative edge detection during segmentation and the lack of depth information in two-dimensional images, which may obscure internal aggregate structure.
BacQuant Enumerates More Cells Than Viable Plate Counts
BacQuant total cell counts were compared to traditional VPC measurements obtained via serial dilution and drop-plating (Figure 1A,E). Across twenty samples, BacQuant produced significantly higher mean cell counts than VPC (p=0.0001), with averages of 1.18 x 1011 and 4.46 x 107 cells, respectively (Figure 1F). BacQuant results were more tightly clustered, whereas VPC values exhibited greater variability.
This discrepancy is consistent with the presence of biofilm aggregates that contain many cells but yield only a single colony on agar plates. Additionally, BacQuant includes dead cells in its estimates, whereas VPC counts only viable cells, further contributing to higher total counts.
Discussion/Conclusion
This study introduces BacQuant, a computationally inexpensive image-processing pipeline for quantifying biofilm cells following sonication. BacQuant addresses a fundamental limitation of CFU-based methods by explicitly accounting for residual aggregates that persist after mechanical disruption and confound viable cell estimates (Fleming et al., 2020). By combining thresholding with edge and contour detection, BacQuant enables reproducible instant segmentation of individual cells and aggregates.
Sonication was selected as the representative disruption method due to its widespread use in laboratory, clinical, and industrial contexts. While sonication effectively detaches biofilms from surfaces, it does not reliably dissociate biofilms into single cells, leaving EPS-bound aggregates intact (Kragh et al., 2016) (Figure 1B). These aggregates are indistinguishable from single cells in VPC assays, leading to systematic underestimation of bacterial load. BacQuant circumvents this issue by estimating the number of cells within aggregates, producing significantly higher and more consistent counts than VPC (Figure 1F).
A major strength of BacQuant is its accessibility. Unlike machine learning-based or confocal approaches, the pipeline does not require training data, specialized hardware, or bulky software installations. It can be run on standard CPUs and adapted to diverse imaging conditions, making it broadly applicable across laboratories. The method captures biologically meaningful structure features, specifically the distinction between free cells and aggregates, that are routinely missed by culture-based techniques.
Nevertheless, BacQuant has limitations. In dense images, over-segmentation may occur, leading to slight overestimation of aggregate numbers (Figure 1D). Conversely, free cells are underestimated due to reliance on an average cell area parameter that does not account for morphological variability (Figure 1C). The pipeline also cannot distinguish between live and dead cells, which may inflate estimates relative to viable counts. If cells occur in small clusters like doublets or triplets, it is processed as an aggregate while they could be a few individual cells in close promixity to one another. Another major limitation is the imposition of 2D structure on the 3D aggregate morphology. Because BacQuant only uses two-dimensional estimation, edge detection is conservative and does not take the height of the structure into account when determining cell density within large cell clusters, dampening the true estimate of cell density within. Additionally, accurate performance depends on high-contrast microscopy images and correct calibration for each experimental setup.
Despite these constraints, BacQuant provides a rapid and scalable complement to traditional microbiological methods. The entire analysis can be completed within minutes, compared to hours or days required for plating assays. Accurate biofilm quantification is critical for antimicrobial testing, clinical research, and environmental monitoring, and BacQuant offers a practical tool for improving structural enumeration of bacterial populations (Folliero et al., 2021).
Future work should integrate live/dead staining, molecular viability markers, and three-dimension imaging to refine estimates of active biofilm populations (Sauer et al., 2022; Wang, Zhu, Zheng, Dong, & Liu, 2022). Incorporation of machine learning-based segmentation could further enhance classification of complex morphologies. Ultimately, BacQuant provides a foundation for automated, image-based biofilm quantification and highlights the value of computer vision approaches in microbiology research..
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","pubmedId":"","doi":"10.1128/aem.01072-22"}],"title":"BacQuant: A Scalable Automated Image Processing Pipeline for Quantifying Biofilm Aggregates
","reviews":[],"curatorReviews":[]},{"id":"7863cbb5-9671-44b1-a55a-433fa1b5afa8","decision":"edit","abstract":"Traditional microbiology methods rely on viable plate counting to quantify bacterial populations but often underestimate biofilm cell density because matrix-encased aggregates can produce a single colony despite containing many cells. Here, we present BacQuant, a computer vision pipeline developed in Python and OpenCV to quantify biofilm aggregates from brightfield microscopy images. Using image thresholding, segmentation, and contour detection, BacQuant distinguishes individual cells from aggregates and estimates total cell burden more comprehensively than viable plate counting alone. Automated counts closely matched manual microscopy counts while producing higher estimated densities, highlighting BacQuant as a scalable, inexpensive complimentary method for biofilm quantification.
","acknowledgements":"","authors":[{"affiliations":["Binghamton University","Binghamton University"],"departments":["First-year Research Immersion Program","Department of Biological Sciences"],"credit":["conceptualization","dataCuration","formalAnalysis","methodology","writing_originalDraft"],"email":"tevinflom@gmail.com","firstName":"Tevin","lastName":"Flom","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","supervision","writing_reviewEditing"],"email":"uciftci@binghamton.edu","firstName":"Umur A. ","lastName":"Ciftci","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","methodology","resources","supervision","writing_reviewEditing"],"email":"clight@binghamton.edu","firstName":"Caitlin J. ","lastName":"Light","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","fundingAcquisition","methodology","project","resources","supervision","writing_originalDraft","writing_reviewEditing","validation"],"email":"wredman@binghamton.edu","firstName":"Whitni K.","lastName":"Redman","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":null},"extendedData":[],"funding":"This work was supported by the First-year Research Immersion Program at Binghamton University, the Binghamton University Scholars Program, and the Harpur’s Edge Award from the External Scholarship and Undergraduate Research Center at Binghamton University.
","image":{"url":"https://portal.micropublication.org/uploads/9f4c7584390c9a3aa2fafe64c2a60b2d.jpg"},"imageCaption":"(A) Biofilm growth, sample preparation, and analysis. 48-hour PAO1 biofilms were collected, rinsed to removed unattached cells, and sonicated to liberate biofilm-encased bacteria. The resulting suspension were mounted on slides, stained with safranin, and imaged by brightfield microscopy. Images were analyzed using the BacQuant pipeline to distinguish free cells from aggregates and determine cell counts. In parallel, cell viability was assessed by serial dilution and plating to obtain CFU/mL measurements. (B) Representative images of stained cell populations. Cells were stained with a safranin-based counterstain for one minute in order to show both free cell and aggregate morphologies. Examples of free cells (A) and aggregates (B) are shown by the labeled arrows. These images were typical of the aforementioned experimental design. Representative of n= 32 images. Scale bars represent 10 µm. (C,D) Validation of the Pipeline Against Ground-Truth Labels. Estimation plots of free cells (C) and aggregates (D) are shown. Ground truth counts were obtained by manually counting the number of free cells and aggregates in each image. The ground truth counts across 10 representative images from the total experimental set of 300 images were measured, and the pipeline was run once on each sample image. The green circles represent ground truth counts, and orange circles represent BacQuant measurements. (E,F) Comparison of BacQuant Against Viable Plate Cell Counts. Viable plate counts were determined via serial dilution and drop-plating of the post-sonicated sample. Representative images of drop-plates and corresponding microscopy images (E), along with the CFU/mL calculated from BacQuant (left column) and viable plate count (right column). Average cells/mL counts from BacQuant (orange bar) were compared against CFU/mL counts from viable plate counts (blue) in (F). The estimation plots above were generated from paired t-tests.; ****, p<0.0001, n=10. Statistical significance was determined with an unpaired t-test, and error bars represent standard deviation. ****, p<0.0001, n=18-22 biofilm samples with 10 images per sample.
","imageTitle":"Work Flow and Representative Results of BacQuant
","methods":"Materials and Methods
Bacterial Culture Preparation
P. aeruginosa PAO1 liquid cultures were routinely grown in lysogeny broth (LB) (SigmaAldrich®, Cat# L3022) at 37°C under 220 RPM shaking conditions for 18 hours in 125 mL flasks.
In vitro Well-plate Model
P. aeruginosa biofilms were cultivated in 24-well non-tissue culture-treated plates (VWR, Cat# 10861-558) for 48 hours at 37°C under 80 RPM shaking conditions. Individual wells were inoculated with 106 CFU/mL in 800 µL. After the 48-hour incubation, media was removed, and each well was rinsed with 1 mL of 0.85% saline solution to remove any unattached or lysed cells. This growth protocol was adapted from Fleming et. al (Fleming et al., 2020). The entire plate was covered with parafilm and placed into a water bath sonicator (Fischer Scientific, Cat# 15337411) for 30 minutes. This sonication condition was selected based on previously published biofilm disruption protocols (Fleming, Chahin, & Rumbaugh, 2017; Redman et al., 2021) and was not used independently optimized in the present study. Because the purpose of this work was to evaluate BacQuant as an image-based method for quantifying cells and residual aggregates following a standard disruption step, additional optimization of sonication intensity, during, or enzymatic pretreatment was outside the scope of this study. After sonication, the contents of each well were moved to a respective 1.5 mL microcentrifuge tube.
Slide Preparation and Viability Assay
After harvesting, 10 µL of cell solution was added onto a glass microscope slide (Globe Scientific, Inc, Cat# 1304). The solution was spread into a 1 cm2 area at the center of each slide using an inoculation loop. The samples were then heat-fixed onto the slides and stained with Safranin Advanced Counterstain (Hardy Diagnostics, Cat# GK400) for 1 minute and subsequently rinsed with deionized water. The slides were then wet-mounted using immersion oil and topped with a glass cover slip. Concurrently, the remaining cell solution was serially diluted in saline and drop-plated onto solid LB agar and incubated for 18-24 hours at 37°C for CFU enumeration.
Brightfield Microscopy
All slides were imaged using an Olympus BX43 microscope under oil immersion at 100x magnification. Five representative images of each slide were taken, representing one well in the 24-well plate model. An Olympus DP22 camera was used with CellSens Entry software to take the images. The size of each image and light intensity was constant across all images. The dimensions of each image were measured to be 5000 x 7000 nm, in metric units using the Linear Ruler tool in CellSens Entry. Images were saved and label-matched to their corresponding drop-plate.
Image Analysis
A custom computer vision image analysis pipeline was built using Python v3.8 programming language. A stand-alone version of the pipeline is provided and can be run as an online notebook in or as a script with a Python IDE. All necessary interaction with the code is clearly labeled for the user as comments in the code, as this pipeline is not based on a graphical user interface (GUI). This pipeline should be adapted for individual experiments, as it is currently optimized for use with P. aeruginosa mono-species biofilm cells. All parameters are clearly labeled in the code and are easily customizable. All code is freely available, with the implementation of each step of the pipeline clearly shown. The code is available through Google Colaboratory (https://colab.research.google.com/drive/1JWpmjoXKtgYmeux7MUyBH6syZLHm1inf?usp=sharing). We recommend this pipeline be used with GPU acceleration for high-throughput analyses, but running the process in the cloud or with a CPU is also easily supported. The pipeline takes .png file format images as the primary input, as well as user-specified parameters such as the area of an individual cell in pixels and image size.
The first step of the pipeline entails generating a binary mask of the image based on color threshold values. The mask eliminates background noise in the image and standardizes the shape and sizes of all image features. Following masking, a diameter-based size thresholding algorithm is used to label large connected regions in the image as aggregates in blue, and smaller regions as free cells in yellow. This simplifies the visual distinction between aggregates and free cells. From this color-changing step, the percentages of aggregated biomass and free cells are determined via color-based pixel pooling. For the purpose of this analysis, an aggregate is defined as any connected segmented region with an estimated diameter greater than the user-defined threshold for a single cell. Segmented regions at or below this threshold are classified as free cells. Smaller clusters like doublets, triplets, and quadruplets are classified according to the same size-based rules and were not assigned separate biological categories. If the connected region exceeds the aggregate threshold, it is labeled as an aggregate and its cell number is estimated by dividing the segmented area by the user defined single-cell average area in pixels. If the region does not exceed the threshold, it is classified as a free-cell region.
Concurrently, a contour detection algorithm from the OpenCV (https://github.com/itseez/opencv) library is used to extract aggregated regions of cells as features. The edges of each aggregate are separated from the background image using Canny edge detection (DOI: 10.1109/TPAMI.1986.4767851). Then, the remaining regions are enclosed by a boundary line through contour detection. This allows for the thorough definition of the 2D shape of each aggregate. From these contours, the area of each aggregate is calculated in pixels and converted to square microns. Cell counts within the aggregates are based on the area of an individual cell in pixels, which is defined by the user at the beginning of the pipeline. It is important to note that this process only works on two-dimensional images. It also can be used with epifluorescence microscopy and live/dead staining with modification in the image processing code to exclude dead cells. It is not optimized for Z-stack images, though it can be modified to work with image arrays and high-throughput imaging results. This makes BacQuant a useful tool for more basic laboratories without complex equipment, software, or imaging capabilities.
The output of this pipeline includes the percent aggregated biomass in each image, number of aggregates, estimated number of cells in aggregates, total estimated cells in each image, and estimated calculation of cells per mL of solution. The raw data from this analysis is stored as a .csv file format spreadsheet. We recommend that further processing of the data be conducted in Microsoft Excel.
Statistical Analysis
Statistical analysis was conducted using GraphPad Prism. A paired t-test was utilized to compare ground truth and BacQuant counts of aggregates and free cells. An unpaired t-test was used to compare viable plate count assay results with BacQuant results. All analyses were conducted to determine statistical significance with an alpha value of 0.05.
","reagents":"","patternDescription":"Biofilms are communities of bacteria encased in a matrix composed of extracellular polymeric substances (EPS) including lipids, proteins, extracellular DNA, and polysaccharides (Flemming et al., 2025). These communities are ubiquitous across medical, industrial, and environmental systems and are responsible for an estimated $4 trillion in global economic damage annually (Camara et al., 2022). Clinically, biofilms contribute to approximately 80% of human infections, where the EPS confers protection from antibiotics and host immune responses (Fedorowski, Moller, & Melander, 2013). Both Gram-positive species, such as Staphylococcus aureus, and Gram-negative species, such as Pseudomonas aeruginosa, exhibit biofilm-associated tolerance and persistence that complicate treatment (Schaber et al., 2007; Usui, Yoshii, Thiriet-Rupert, Ghigo, & Beloin, 2023).
A major challenge in biofilm research is accurate quantification of bacterial populations following experimental treatment. Conventional colony-forming unit (CFU) or viable plate counting (VPC) methods remain widely used because they specifically measure culturable, viable bacteria. However, in biofilm samples, VPC may not fully reflect the total number of cells present because aggregates containing multiple cells can produce a single colony if they are not completely freed from the matrix before plating (Beal et al., 2020; Martini, Boddu, Nemenman, & Vega, 2024). Mechanical disruption methods, such as homogenization or sonication, are commonly used to break up biofilms prior to plating. While optimized detachment and sonication protocols can substantially improve biofilm separation, incomplete dissociation of EPS-bound aggregates may still occur depending on the organism, biofilm structure, treatments, and disruption protocol used (Buckingham-Meyer et al., 2022; Korshoj & Kielian, 2024).
Recent advances in computer vision and microscopy offer alternative strategies for microbial quantification. Image-based methods can distinguish individual cells from aggregates using segmentation and contour detection, providing structural information that is lost in culture-based approaches (Holicheva et al., 2025; J. Wang et al., 2022). However, existing tools such as BiofilmQ and confocal-based pipelines are computationally intensive, require specialized hardware, or are not designed for post-disruption enumeration (Hartmann et al., 2021; Mountcastle et al., 2021).
Here, we present BacQuant, a computationally inexpensive image processing pipeline to improve quantification of biofilm cells following sonication using brightfield microscopy. Using P. aeruginosa as a model organism, BacQuant differentiates free cells from aggregates and estimates aggregate cell numbers to improve quantification relative to VPC methods (Figure 1A). It is important to note that BacQuant does not intend to count viable cells. Rather, it estimates the total cell count in the biofilm sample including both live and dead cells. It does not estimate live cells like traditional VPC methods do.
Definition of Free Cells and Aggregates
Sonication of P. aeruginosa biofilms consistently yields two morphologies – free individual cells and EPS-encased aggregates (Figure 1B). Operationally, BacQuant defines these categories by segmented object size rather than by visual interpretation alone. Free cells displayed relatively uniform size and shape, whereas aggregates varied widely in morphology, often appearing circular with irregular edges and measuring up to ~1000 µm in diameter. Safranin staining revealed higher color saturation in aggregates, reflecting increased biomass and EPS. Both morphologies were present in all samples, indicating incomplete disaggregation by sonication.
BacQuant Aggregate Counts Do Not Differ From Ground Truth
To evaluate BacQuant performance, images were treated as biological replicates and manually annotated to generate ground truth labels. There was a significant difference (p=0.0001; Figure 1C) in the number of free cells identified by ground truth and those identified by BacQuant. This underestimation likely arises from pixel-based normalization to a user-defined average cell area, which does not account for cell orientation or morphological variability.
In contrast, BacQuant slightly overestimated aggregate counts compared to ground truth, although this difference was not statistically significant (p=0.0569; Figure 1D). This trend likely reflects conservative edge detection during segmentation and the lack of depth information in two-dimensional images, which may obscure internal aggregate structure.
BacQuant Enumerates More Cells Than Viable Plate Counts
BacQuant total cell counts were compared to traditional VPC measurements obtained via serial dilution and drop-plating (Figure 1A,E). Across twenty samples, BacQuant produced significantly higher mean cell counts than VPC (p=0.0001), with averages of 1.18 x 1011 and 4.46 x 107 cells, respectively (Figure 1F). BacQuant results were more tightly clustered, whereas VPC values exhibited greater variability.
This discrepancy is consistent with the presence of biofilm aggregates that contain many cells but yield only a single colony on agar plates. Additionally, BacQuant includes dead cells in its estimates, whereas VPC counts only viable cells, further contributing to higher total counts.
Discussion/Conclusion
This study introduces BacQuant, a computationally inexpensive image-processing pipeline for quantifying biofilm cells following sonication. BacQuant addresses a fundamental limitation of CFU-based methods by explicitly accounting for residual aggregates that persist after mechanical disruption and confound viable cell estimates (Fleming et al., 2020). By combining thresholding with edge and contour detection, BacQuant enables reproducible instant segmentation of individual cells and aggregates.
Sonication was selected as the representative disruption method due to its widespread use in laboratory, clinical, and industrial contexts. While sonication effectively detaches biofilms from surfaces, it does not reliably dissociate biofilms into single cells, leaving EPS-bound aggregates intact (Kragh et al., 2016) (Figure 1B). These aggregates are indistinguishable from single cells in VPC assays, leading to systematic underestimation of bacterial load. BacQuant circumvents this issue by estimating the number of cells within aggregates, producing significantly higher and more consistent counts than VPC (Figure 1F).
A major strength of BacQuant is its accessibility. Unlike machine learning-based or confocal approaches, the pipeline does not require training data, specialized hardware, or bulky software installations. It can be run on standard CPUs and adapted to diverse imaging conditions, making it broadly applicable across laboratories. The method captures biologically meaningful structure features, specifically the distinction between free cells and aggregates, that are routinely missed by culture-based techniques.
Nevertheless, BacQuant has limitations. In dense images, over-segmentation may occur, leading to slight overestimation of aggregate numbers (Figure 1D). Conversely, free cells are underestimated due to reliance on an average cell area parameter that does not account for morphological variability (Figure 1C). The pipeline also cannot distinguish between live and dead cells, which may inflate estimates relative to viable counts. If cells occur in small clusters like doublets or triplets, it is processed as an aggregate while they could be a few individual cells in close promixity to one another. Another major limitation is the imposition of 2D structure on the 3D aggregate morphology. Because BacQuant only uses two-dimensional estimation, edge detection is conservative and does not take the height of the structure into account when determining cell density within large cell clusters, dampening the true estimate of cell density within. Additionally, accurate performance depends on high-contrast microscopy images and correct calibration for each experimental setup.
Despite these constraints, BacQuant provides a rapid and scalable complement to traditional microbiological methods. The entire analysis can be completed within minutes, compared to hours or days required for plating assays. Accurate biofilm quantification is critical for antimicrobial testing, clinical research, and environmental monitoring, and BacQuant offers a practical tool for improving structural enumeration of bacterial populations (Folliero et al., 2021).
Future work should integrate live/dead staining, molecular viability markers, and three-dimension imaging to refine estimates of active biofilm populations (Sauer et al., 2022; Wang, Zhu, Zheng, Dong, & Liu, 2022). Incorporation of machine learning-based segmentation could further enhance classification of complex morphologies. Ultimately, BacQuant provides a foundation for automated, image-based biofilm quantification and highlights the value of computer vision approaches in microbiology research..
","references":[{"reference":"Acosta N, Waddell B, Heirali A, Somayaji R, Surette MG, Workentine ML, Rabin HR, Parkins MD. 2020. Cystic Fibrosis Patients Infected With Epidemic Pseudomonas aeruginosa Strains Have Unique Microbial Communities. Frontiers in Cellular and Infection Microbiology 10: 10.3389/fcimb.2020.00173.
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","pubmedId":"","doi":"10.1155/2021/9033278"},{"reference":"Fowler TE, Bloomquist RF, Sakhalkar MV, Bloomquist DT. 2023. Chronic Purulent Conjunctivitis Associated With Extensively Drug-Resistant Pseudomonas aeruginosa. JAMA Ophthalmology 141: 609.
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","pubmedId":"","doi":"10.1093/bioinformatics/btac529"},{"reference":"Wang S, Zhu H, Zheng G, Dong F, Liu C. 2022. Dynamic Changes in Biofilm Structures under Dynamic Flow Conditions. Applied and Environmental Microbiology 88: 10.1128/aem.01072-22.
","pubmedId":"","doi":"10.1128/aem.01072-22"}],"title":"BacQuant: A Scalable Automated Image Processing Pipeline for Quantifying Biofilm Aggregates
","reviews":[],"curatorReviews":[]},{"id":"9dda7ccf-1124-4a61-9c7a-eb8a9f31818a","decision":"revise","abstract":"Traditional microbiology methods rely on viable plate counting to quantify bacterial populations but often underestimate biofilm cell density because matrix-encased aggregates can produce a single colony despite containing many cells. Here, we present BacQuant, a computer vision pipeline developed in Python and OpenCV to quantify biofilm aggregates from brightfield microscopy images. Using image thresholding, segmentation, and contour detection, BacQuant distinguishes individual cells from aggregates and estimates total cell burden more comprehensively than viable plate counting alone. Automated counts closely matched manual microscopy counts while producing higher estimated densities, highlighting BacQuant as a scalable, inexpensive complimentary method for biofilm quantification.
","acknowledgements":"","authors":[{"affiliations":["Binghamton University","Binghamton University"],"departments":["First-year Research Immersion Program","Department of Biological Sciences"],"credit":["conceptualization","dataCuration","formalAnalysis","methodology","writing_originalDraft"],"email":"tevinflom@gmail.com","firstName":"Tevin","lastName":"Flom","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","supervision","writing_reviewEditing"],"email":"uciftci@binghamton.edu","firstName":"Umur A. ","lastName":"Ciftci","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","methodology","resources","supervision","writing_reviewEditing"],"email":"clight@binghamton.edu","firstName":"Caitlin J. ","lastName":"Light","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","fundingAcquisition","methodology","project","resources","supervision","writing_originalDraft","writing_reviewEditing","validation"],"email":"wredman@binghamton.edu","firstName":"Whitni K.","lastName":"Redman","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":null},"extendedData":[],"funding":"This work was supported by the First-year Research Immersion Program at Binghamton University, the Binghamton University Scholars Program, and the Harpur’s Edge Award from the External Scholarship and Undergraduate Research Center at Binghamton University.
","image":{"url":"https://portal.micropublication.org/uploads/9f4c7584390c9a3aa2fafe64c2a60b2d.jpg"},"imageCaption":"(A) Biofilm growth, sample preparation, and analysis. 48-hour PAO1 biofilms were collected, rinsed to removed unattached cells, and sonicated to liberate biofilm-encased bacteria. The resulting suspension were mounted on slides, stained with safranin, and imaged by brightfield microscopy. Images were analyzed using the BacQuant pipeline to distinguish free cells from aggregates and determine cell counts. In parallel, cell viability was assessed by serial dilution and plating to obtain CFU/mL measurements. (B) Representative images of stained cell populations. Cells were stained with a safranin-based counterstain for one minute in order to show both free cell and aggregate morphologies. Examples of free cells (A) and aggregates (B) are shown by the labeled arrows. These images were typical of the aforementioned experimental design. Representative of n= 32 images. Scale bars represent 10 µm. (C,D) Validation of the Pipeline Against Ground-Truth Labels. Estimation plots of free cells (C) and aggregates (D) are shown. Ground truth counts were obtained by manually counting the number of free cells and aggregates in each image. The ground truth counts across 10 representative images from the total experimental set of 300 images were measured, and the pipeline was run once on each sample image. The green circles represent ground truth counts, and orange circles represent BacQuant measurements. (E,F) Comparison of BacQuant Against Viable Plate Cell Counts. Viable plate counts were determined via serial dilution and drop-plating of the post-sonicated sample. Representative images of drop-plates and corresponding microscopy images (E), along with the CFU/mL calculated from BacQuant (left column) and viable plate count (right column). Average cells/mL counts from BacQuant (orange bar) were compared against CFU/mL counts from viable plate counts (blue) in (F). The estimation plots above were generated from paired t-tests.; ****, p<0.0001, n=10. Statistical significance was determined with an unpaired t-test, and error bars represent standard deviation. ****, p<0.0001, n=18-22 biofilm samples with 10 images per sample.
","imageTitle":"Work Flow and Representative Results of BacQuant
","methods":"Materials and Methods
Bacterial Culture Preparation
P. aeruginosa PAO1 liquid cultures were routinely grown in lysogeny broth (LB) (SigmaAldrich®, Cat# L3022) at 37°C under 220 RPM shaking conditions for 18 hours in 125 mL flasks.
In vitro Well-plate Model
P. aeruginosa biofilms were cultivated in 24-well non-tissue culture-treated plates (VWR, Cat# 10861-558) for 48 hours at 37°C under 80 RPM shaking conditions. Individual wells were inoculated with 106 CFU/mL in 800 µL. After the 48-hour incubation, media was removed, and each well was rinsed with 1 mL of 0.85% saline solution to remove any unattached or lysed cells. This growth protocol was adapted from Fleming et. al (Fleming et al., 2020). The entire plate was covered with parafilm and placed into a water bath sonicator (Fischer Scientific, Cat# 15337411) for 30 minutes. This sonication condition was selected based on previously published biofilm disruption protocols (Fleming, Chahin, & Rumbaugh, 2017; Redman et al., 2021) and was not used independently optimized in the present study. Because the purpose of this work was to evaluate BacQuant as an image-based method for quantifying cells and residual aggregates following a standard disruption step, additional optimization of sonication intensity, during, or enzymatic pretreatment was outside the scope of this study. After sonication, the contents of each well were moved to a respective 1.5 mL microcentrifuge tube.
Slide Preparation and Viability Assay
After harvesting, 10 µL of cell solution was added onto a glass microscope slide (Globe Scientific, Inc, Cat# 1304). The solution was spread into a 1 cm2 area at the center of each slide using an inoculation loop. The samples were then heat-fixed onto the slides and stained with Safranin Advanced Counterstain (Hardy Diagnostics, Cat# GK400) for 1 minute and subsequently rinsed with deionized water. The slides were then wet-mounted using immersion oil and topped with a glass cover slip. Concurrently, the remaining cell solution was serially diluted in saline and drop-plated onto solid LB agar and incubated for 18-24 hours at 37°C for CFU enumeration.
Brightfield Microscopy
All slides were imaged using an Olympus BX43 microscope under oil immersion at 100x magnification. Five representative images of each slide were taken, representing one well in the 24-well plate model. An Olympus DP22 camera was used with CellSens Entry software to take the images. The size of each image and light intensity was constant across all images. The dimensions of each image were measured to be 5000 x 7000 nm, in metric units using the Linear Ruler tool in CellSens Entry. Images were saved and label-matched to their corresponding drop-plate.
Image Analysis
A custom computer vision image analysis pipeline was built using Python v3.8 programming language. A stand-alone version of the pipeline is provided and can be run as an online notebook in or as a script with a Python IDE. All necessary interaction with the code is clearly labeled for the user as comments in the code, as this pipeline is not based on a graphical user interface (GUI). This pipeline should be adapted for individual experiments, as it is currently optimized for use with P. aeruginosa mono-species biofilm cells. All parameters are clearly labeled in the code and are easily customizable. All code is freely available, with the implementation of each step of the pipeline clearly shown. The code is available through Google Colaboratory (https://colab.research.google.com/drive/1JWpmjoXKtgYmeux7MUyBH6syZLHm1inf?usp=sharing). We recommend this pipeline be used with GPU acceleration for high-throughput analyses, but running the process in the cloud or with a CPU is also easily supported. The pipeline takes .png file format images as the primary input, as well as user-specified parameters such as the area of an individual cell in pixels and image size.
The first step of the pipeline entails generating a binary mask of the image based on color threshold values. The mask eliminates background noise in the image and standardizes the shape and sizes of all image features. Following masking, a diameter-based size thresholding algorithm is used to label large connected regions in the image as aggregates in blue, and smaller regions as free cells in yellow. This simplifies the visual distinction between aggregates and free cells. From this color-changing step, the percentages of aggregated biomass and free cells are determined via color-based pixel pooling. For the purpose of this analysis, an aggregate is defined as any connected segmented region with an estimated diameter greater than the user-defined threshold for a single cell. Segmented regions at or below this threshold are classified as free cells. Smaller clusters like doublets, triplets, and quadruplets are classified according to the same size-based rules and were not assigned separate biological categories. If the connected region exceeds the aggregate threshold, it is labeled as an aggregate and its cell number is estimated by dividing the segmented area by the user defined single-cell average area in pixels. If the region does not exceed the threshold, it is classified as a free-cell region.
Concurrently, a contour detection algorithm from the OpenCV (https://github.com/itseez/opencv) library is used to extract aggregated regions of cells as features. The edges of each aggregate are separated from the background image using Canny edge detection (DOI: 10.1109/TPAMI.1986.4767851). Then, the remaining regions are enclosed by a boundary line through contour detection. This allows for the thorough definition of the 2D shape of each aggregate. From these contours, the area of each aggregate is calculated in pixels and converted to square microns. Cell counts within the aggregates are based on the area of an individual cell in pixels, which is defined by the user at the beginning of the pipeline. It is important to note that this process only works on two-dimensional images. It also can be used with epifluorescence microscopy and live/dead staining with modification in the image processing code to exclude dead cells. It is not optimized for Z-stack images, though it can be modified to work with image arrays and high-throughput imaging results. This makes BacQuant a useful tool for more basic laboratories without complex equipment, software, or imaging capabilities.
The output of this pipeline includes the percent aggregated biomass in each image, number of aggregates, estimated number of cells in aggregates, total estimated cells in each image, and estimated calculation of cells per mL of solution. The raw data from this analysis is stored as a .csv file format spreadsheet. We recommend that further processing of the data be conducted in Microsoft Excel.
Statistical Analysis
Statistical analysis was conducted using GraphPad Prism. A paired t-test was utilized to compare ground truth and BacQuant counts of aggregates and free cells. An unpaired t-test was used to compare viable plate count assay results with BacQuant results. All analyses were conducted to determine statistical significance with an alpha value of 0.05.
","reagents":"","patternDescription":"Biofilms are communities of bacteria encased in a matrix composed of extracellular polymeric substances (EPS) including lipids, proteins, extracellular DNA, and polysaccharides (Flemming et al., 2025). These communities are ubiquitous across medical, industrial, and environmental systems and are responsible for an estimated $4 trillion in global economic damage annually (Camara et al., 2022). Clinically, biofilms contribute to approximately 80% of human infections, where the EPS confers protection from antibiotics and host immune responses (Fedorowski, Moller, & Melander, 2013). Both Gram-positive species, such as Staphylococcus aureus, and Gram-negative species, such as Pseudomonas aeruginosa, exhibit biofilm-associated tolerance and persistence that complicate treatment (Schaber et al., 2007; Usui, Yoshii, Thiriet-Rupert, Ghigo, & Beloin, 2023).
A major challenge in biofilm research is accurate quantification of bacterial populations following experimental treatment. Conventional colony-forming unit (CFU) or viable plate counting (VPC) methods remain widely used because they specifically measure culturable, viable bacteria. However, in biofilm samples, VPC may not fully reflect the total number of cells present because aggregates containing multiple cells can produce a single colony if they are not completely freed from the matrix before plating (Beal et al., 2020; Martini, Boddu, Nemenman, & Vega, 2024). Mechanical disruption methods, such as homogenization or sonication, are commonly used to break up biofilms prior to plating. While optimized detachment and sonication protocols can substantially improve biofilm separation, incomplete dissociation of EPS-bound aggregates may still occur depending on the organism, biofilm structure, treatments, and disruption protocol used (Buckingham-Meyer et al., 2022; Korshoj & Kielian, 2024).
Recent advances in computer vision and microscopy offer alternative strategies for microbial quantification. Image-based methods can distinguish individual cells from aggregates using segmentation and contour detection, providing structural information that is lost in culture-based approaches (Holicheva et al., 2025; J. Wang et al., 2022). However, existing tools such as BiofilmQ and confocal-based pipelines are computationally intensive, require specialized hardware, or are not designed for post-disruption enumeration (Hartmann et al., 2021; Mountcastle et al., 2021).
Here, we present BacQuant, a computationally inexpensive image processing pipeline to improve quantification of biofilm cells following sonication using brightfield microscopy. Using P. aeruginosa as a model organism, BacQuant differentiates free cells from aggregates and estimates aggregate cell numbers to improve quantification relative to VPC methods (Figure 1A). It is important to note that BacQuant does not intend to count viable cells. Rather, it estimates the total cell count in the biofilm sample including both live and dead cells. It does not estimate live cells like traditional VPC methods do.
Sonication of P. aeruginosa biofilms consistently yields two morphologies – free individual cells and EPS-encased aggregates (Figure 1B). Operationally, BacQuant defines these categories by segmented object size rather than by visual interpretation alone. Free cells displayed relatively uniform size and shape, whereas aggregates varied widely in morphology, often appearing circular with irregular edges and measuring up to ~1000 µm in diameter. Safranin staining revealed higher color saturation in aggregates, reflecting increased biomass and EPS. Both morphologies were present in all samples, indicating incomplete disaggregation by sonication.
To evaluate BacQuant performance, images were treated as biological replicates and manually annotated to generate ground truth labels. There was a significant difference (p=0.0001; Figure 1C) in the number of free cells identified by ground truth and those identified by BacQuant. This underestimation likely arises from pixel-based normalization to a user-defined average cell area, which does not account for cell orientation or morphological variability.
In contrast, BacQuant slightly overestimated aggregate counts compared to ground truth, although this difference was not statistically significant (p=0.0569; Figure 1D). This trend likely reflects conservative edge detection during segmentation and the lack of depth information in two-dimensional images, which may obscure internal aggregate structure.
BacQuant total cell counts were compared to traditional VPC measurements obtained via serial dilution and drop-plating (Figure 1A,E). Across twenty samples, BacQuant produced significantly higher mean cell counts than VPC (p=0.0001), with averages of 1.18 x 1011 and 4.46 x 107 cells, respectively (Figure 1F). BacQuant results were more tightly clustered, whereas VPC values exhibited greater variability.
This discrepancy is consistent with the presence of biofilm aggregates that contain many cells but yield only a single colony on agar plates. Additionally, BacQuant includes dead cells in its estimates, whereas VPC counts only viable cells, further contributing to higher total counts.
This study introduces BacQuant, a computationally inexpensive image-processing pipeline for quantifying biofilm cells following sonication. BacQuant addresses a fundamental limitation of CFU-based methods by explicitly accounting for residual aggregates that persist after mechanical disruption and confound viable cell estimates (Fleming et al., 2020). By combining thresholding with edge and contour detection, BacQuant enables reproducible instant segmentation of individual cells and aggregates.
Sonication was selected as the representative disruption method due to its widespread use in laboratory, clinical, and industrial contexts. While sonication effectively detaches biofilms from surfaces, it does not reliably dissociate biofilms into single cells, leaving EPS-bound aggregates intact (Kragh et al., 2016) (Figure 1B). These aggregates are indistinguishable from single cells in VPC assays, leading to systematic underestimation of bacterial load. BacQuant circumvents this issue by estimating the number of cells within aggregates, producing significantly higher and more consistent counts than VPC (Figure 1F).
A major strength of BacQuant is its accessibility. Unlike machine learning-based or confocal approaches, the pipeline does not require training data, specialized hardware, or bulky software installations. It can be run on standard CPUs and adapted to diverse imaging conditions, making it broadly applicable across laboratories. The method captures biologically meaningful structure features, specifically the distinction between free cells and aggregates, that are routinely missed by culture-based techniques.
Nevertheless, BacQuant has limitations. In dense images, over-segmentation may occur, leading to slight overestimation of aggregate numbers (Figure 1D). Conversely, free cells are underestimated due to reliance on an average cell area parameter that does not account for morphological variability (Figure 1C). The pipeline also cannot distinguish between live and dead cells, which may inflate estimates relative to viable counts. If cells occur in small clusters like doublets or triplets, it is processed as an aggregate while they could be a few individual cells in close promixity to one another. Another major limitation is the imposition of 2D structure on the 3D aggregate morphology. Because BacQuant only uses two-dimensional estimation, edge detection is conservative and does not take the height of the structure into account when determining cell density within large cell clusters, dampening the true estimate of cell density within. Additionally, accurate performance depends on high-contrast microscopy images and correct calibration for each experimental setup.
Despite these constraints, BacQuant provides a rapid and scalable complement to traditional microbiological methods. The entire analysis can be completed within minutes, compared to hours or days required for plating assays. Accurate biofilm quantification is critical for antimicrobial testing, clinical research, and environmental monitoring, and BacQuant offers a practical tool for improving structural enumeration of bacterial populations (Folliero et al., 2021).
Future work should integrate live/dead staining, molecular viability markers, and three-dimension imaging to refine estimates of active biofilm populations (Sauer et al., 2022; Wang, Zhu, Zheng, Dong, & Liu, 2022). Incorporation of machine learning-based segmentation could further enhance classification of complex morphologies. Ultimately, BacQuant provides a foundation for automated, image-based biofilm quantification and highlights the value of computer vision approaches in microbiology research..
","references":[{"reference":"Acosta N, Waddell B, Heirali A, Somayaji R, Surette MG, Workentine ML, Rabin HR, Parkins MD. 2020. Cystic Fibrosis Patients Infected With Epidemic Pseudomonas aeruginosa Strains Have Unique Microbial Communities. Frontiers in Cellular and Infection Microbiology 10: 10.3389/fcimb.2020.00173.
","pubmedId":"","doi":"10.3389/fcimb.2020.00173"},{"reference":"Beal J, Farny NG, Haddock-Angelli T, Selvarajah V, Baldwin GS, Buckley-Taylor R, et al., Zhou. 2020. Robust estimation of bacterial cell count from optical density. Communications Biology 3: 10.1038/s42003-020-01127-5.
","pubmedId":"","doi":"10.1038/s42003-020-01127-5"},{"reference":"Bjerkan G, Witsø E, Bergh Kr. 2009. Sonication is superior to scraping for retrieval of bacteria in biofilm on titanium and steel surfaces in vitro. Acta Orthopaedica 80: 245-250.
","pubmedId":"","doi":"10.3109/17453670902947457"},{"reference":"Buckingham-Meyer K, Miller LA, Parker AE, Walker DK, Sturman P, Novak I, Goeres DM. 2022. Harvesting and Disaggregation: An Overlooked Step in Biofilm Methods Research. Journal of Visualized Experiments : 10.3791/62390.
","pubmedId":"","doi":"10.3791/62390"},{"reference":"Cámara M, Green W, MacPhee CE, Rakowska PD, Raval R, Richardson MC, et al., Webb. 2022. Economic significance of biofilms: a multidisciplinary and cross-sectoral challenge. npj Biofilms and Microbiomes 8: 10.1038/s41522-022-00306-y.
","pubmedId":"","doi":"10.1038/s41522-022-00306-y"},{"reference":"Fedorowski A, Möller SJ, Melander O. 2013. Response to the letter by prof.
Fleming D, Chahin L, Rumbaugh K. 2017. Glycoside Hydrolases Degrade Polymicrobial Bacterial Biofilms in Wounds. Antimicrobial Agents and Chemotherapy 61: 10.1128/aac.01998-16.
","pubmedId":"27872074","doi":"10.1128/AAC.01998-16 "},{"reference":"Fleming D, Redman W, Welch GS, Mdluli NV, Rouchon CN, Frank KL, Rumbaugh KP. 2020. Utilizing glycoside hydrolases to improve the quantitation and visualization of biofilm bacteria. Biofilm 2: 100037.
","pubmedId":"","doi":"10.1016/j.bioflm.2020.100037"},{"reference":"Flemming HC, van Hullebusch ED, Little BJ, Neu TR, Nielsen PH, Seviour T, et al., Wuertz. 2024. Microbial extracellular polymeric substances in the environment, technology and medicine. Nature Reviews Microbiology 23: 87-105.
","pubmedId":"","doi":"10.1038/s41579-024-01098-y"},{"reference":"Folliero V, Franci G, Dell’Annunziata F, Giugliano R, Foglia F, Sperlongano R, et al., Galdiero. 2021. Evaluation of Antibiotic Resistance and Biofilm Production among Clinical Strain Isolated from Medical Devices. International Journal of Microbiology 2021: 1-11.
","pubmedId":"","doi":"10.1155/2021/9033278"},{"reference":"Fowler TE, Bloomquist RF, Sakhalkar MV, Bloomquist DT. 2023. Chronic Purulent Conjunctivitis Associated With Extensively Drug-Resistant Pseudomonas aeruginosa. JAMA Ophthalmology 141: 609.
","pubmedId":"","doi":"10.1001/jamaophthalmol.2023.1529"},{"reference":"Goldufsky J, Wood SJ, Jayaraman V, Majdobeh O, Chen L, Qin S, et al., Shafikhani. 2015. Pseudomonas aeruginosa uses T3SS to inhibit diabetic wound healing. Wound Repair and Regeneration 23: 557-564.
","pubmedId":"","doi":"10.1111/wrr.12310"},{"reference":"Gonzalez MR, Fleuchot B, Lauciello L, Jafari P, Applegate LA, Raffoul W, Que YA, Perron K. 2016. Effect of Human Burn Wound Exudate on Pseudomonas aeruginosa Virulence. mSphere 1: 10.1128/msphere.00111-15.
","pubmedId":"","doi":"10.1128/mSphere.00111-15"},{"reference":"Hartmann R, Jeckel H, Jelli E, Singh PK, Vaidya S, Bayer M, et al., Drescher. 2021. Quantitative image analysis of microbial communities with BiofilmQ. Nature Microbiology 6: 151-156.
","pubmedId":"","doi":"10.1038/s41564-020-00817-4"},{"reference":"Holicheva AA, Kozlov KS, Boiko DA, Kamanin MS, Provotorova DV, Kolomoets NI, Ananikov VP. 2025. Deep generative modeling of annotated bacterial biofilm images. npj Biofilms and Microbiomes 11: 10.1038/s41522-025-00647-4.
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","pubmedId":"","doi":"10.1371/journal.pone.0154937"},{"reference":"Korshoj LE, Kielian T. 2024. Bacterial single-cell RNA sequencing captures biofilm transcriptional heterogeneity and differential responses to immune pressure. bioRxiv: pii: 2024.06.28.601229. 10.1101/2024.06.28.601229.
","pubmedId":"38979200","doi":""},{"reference":"Kragh KN, Hutchison JB, Melaugh G, Rodesney C, Roberts AEL, Irie Y, et al., Bjarnsholt. 2016. Role of Multicellular Aggregates in Biofilm Formation. mBio 7: 10.1128/mbio.00237-16.
","pubmedId":"","doi":"10.1128/mBio.00237-16"},{"reference":"Martini KM, Boddu SS, Nemenman I, Vega NM. 2024. Maximum likelihood estimators for colony-forming units. Microbiology Spectrum 12: 10.1128/spectrum.03946-23.
","pubmedId":"","doi":"10.1128/spectrum.03946-23"},{"reference":"Mountcastle SE, Vyas N, Villapun VM, Cox SC, Jabbari S, Sammons RL, et al., Kuehne. 2021. Biofilm viability checker: An open-source tool for automated biofilm viability analysis from confocal microscopy images. npj Biofilms and Microbiomes 7: 10.1038/s41522-021-00214-7.
","pubmedId":"","doi":"10.1038/s41522-021-00214-7"},{"reference":"Redman WK, Welch GS, Williams AC, Damron AJ, Northcut WO, Rumbaugh KP. 2021. Efficacy and safety of biofilm dispersal by glycoside hydrolases in wounds. Biofilm 3: 100061.
","pubmedId":"34825176","doi":"10.1016/j.bioflm.2021.100061 "},{"reference":"Sauer K, Stoodley P, Goeres DM, Hall-Stoodley L, Burmølle M, Stewart PS, Bjarnsholt T. 2022. The biofilm life cycle: expanding the conceptual model of biofilm formation. Nature Reviews Microbiology 20: 608-620.
","pubmedId":"","doi":"10.1038/s41579-022-00767-0"},{"reference":"Schaber JA, Triffo WJ, Suh SJ, Oliver JW, Hastert MC, Griswold JA, et al., Rumbaugh. 2007. Pseudomonas aeruginosa\n Forms Biofilms in Acute Infection Independent of Cell-to-Cell Signaling. Infection and Immunity 75: 3715-3721.
","pubmedId":"","doi":"10.1128/IAI.00586-07"},{"reference":"Stoodley P, Sauer K, Davies DG, Costerton JW. 2002. Biofilms as Complex Differentiated Communities. Annual Review of Microbiology 56: 187-209.
","pubmedId":"","doi":"10.1146/annurev.micro.56.012302.160705"},{"reference":"Usui M, Yoshii Y, Thiriet-Rupert S, Ghigo JM, Beloin C. 2023. Intermittent antibiotic treatment of bacterial biofilms favors the rapid evolution of resistance. Communications Biology 6: 10.1038/s42003-023-04601-y.
","pubmedId":"","doi":"10.1038/s42003-023-04601-y"},{"reference":"Wang J, Tabassum N, Toma TT, Wang Y, Gahlmann A, Acton ST. 2022. 3D GAN image synthesis and dataset quality assessment for bacterial biofilm. Bioinformatics 38: 4598-4604.
","pubmedId":"","doi":"10.1093/bioinformatics/btac529"},{"reference":"Wang S, Zhu H, Zheng G, Dong F, Liu C. 2022. Dynamic Changes in Biofilm Structures under Dynamic Flow Conditions. Applied and Environmental Microbiology 88: 10.1128/aem.01072-22.
","pubmedId":"","doi":"10.1128/aem.01072-22"}],"title":"BacQuant: A Scalable Automated Image Processing Pipeline for Quantifying Biofilm Aggregates
","reviews":[],"curatorReviews":[]},{"id":"6f8ea0ac-ed94-425f-86ee-5b74022a0557","decision":"publish","abstract":"Traditional microbiology methods rely on viable plate counting to quantify bacterial populations but often underestimate biofilm cell density because matrix-encased aggregates can produce a single colony despite containing many cells. Here, we present BacQuant, a computer vision pipeline developed in Python and OpenCV to quantify biofilm aggregates from brightfield microscopy images. Using image thresholding, segmentation, and contour detection, BacQuant distinguishes individual cells from aggregates and estimates total cell burden more comprehensively than viable plate counting alone. Automated counts closely matched manual microscopy counts while producing higher estimated densities, highlighting BacQuant as a scalable, inexpensive complimentary method for biofilm quantification.
","acknowledgements":"","authors":[{"affiliations":["Binghamton University","Binghamton University"],"departments":["First-year Research Immersion Program","Department of Biological Sciences"],"credit":["conceptualization","dataCuration","formalAnalysis","methodology","writing_originalDraft"],"email":"tevinflom@gmail.com","firstName":"Tevin","lastName":"Flom","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","supervision","writing_reviewEditing"],"email":"uciftci@binghamton.edu","firstName":"Umur A. ","lastName":"Ciftci","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","methodology","resources","supervision","writing_reviewEditing"],"email":"clight@binghamton.edu","firstName":"Caitlin J. ","lastName":"Light","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null},{"affiliations":["Binghamton University"],"departments":["First-year Research Immersion Program"],"credit":["conceptualization","formalAnalysis","fundingAcquisition","methodology","project","resources","supervision","writing_originalDraft","writing_reviewEditing","validation"],"email":"wredman@binghamton.edu","firstName":"Whitni K.","lastName":"Redman","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":null},"extendedData":[],"funding":"This work was supported by the First-year Research Immersion Program at Binghamton University, the Binghamton University Scholars Program, and the Harpur’s Edge Award from the External Scholarship and Undergraduate Research Center at Binghamton University.
","image":{"url":"https://portal.micropublication.org/uploads/9f4c7584390c9a3aa2fafe64c2a60b2d.jpg"},"imageCaption":"(A) Biofilm growth, sample preparation, and analysis. 48-hour PAO1 biofilms were collected, rinsed to removed unattached cells, and sonicated to liberate biofilm-encased bacteria. The resulting suspension were mounted on slides, stained with safranin, and imaged by brightfield microscopy. Images were analyzed using the BacQuant pipeline to distinguish free cells from aggregates and determine cell counts. In parallel, cell viability was assessed by serial dilution and plating to obtain CFU/mL measurements. (B) Representative images of stained cell populations. Cells were stained with a safranin-based counterstain for one minute in order to show both free cell and aggregate morphologies. Examples of free cells (A) and aggregates (B) are shown by the labeled arrows. These images were typical of the aforementioned experimental design. Representative of n= 32 images. Scale bars represent 10 µm. (C,D) Validation of the Pipeline Against Ground-Truth Labels. Estimation plots of free cells (C) and aggregates (D) are shown. Ground truth counts were obtained by manually counting the number of free cells and aggregates in each image. The ground truth counts across 10 representative images from the total experimental set of 300 images were measured, and the pipeline was run once on each sample image. The green circles represent ground truth counts, and orange circles represent BacQuant measurements. (E,F) Comparison of BacQuant Against Viable Plate Cell Counts. Viable plate counts were determined via serial dilution and drop-plating of the post-sonicated sample. Representative images of drop-plates and corresponding microscopy images (E), along with the CFU/mL calculated from BacQuant (left column) and viable plate count (right column). Average cells/mL counts from BacQuant (orange bar) were compared against CFU/mL counts from viable plate counts (blue) in (F). The estimation plots above were generated from paired t-tests.; ****, p<0.0001, n=10. Statistical significance was determined with an unpaired t-test, and error bars represent standard deviation. ****, p<0.0001, n=18-22 biofilm samples with 10 images per sample.
","imageTitle":"Work Flow and Representative Results of BacQuant
","methods":"Materials and Methods
Bacterial Culture Preparation
P. aeruginosa PAO1 liquid cultures were routinely grown in lysogeny broth (LB) (SigmaAldrich®, Cat# L3022) at 37°C under 220 RPM shaking conditions for 18 hours in 125 mL flasks.
In vitro Well-plate Model
P. aeruginosa biofilms were cultivated in 24-well non-tissue culture-treated plates (VWR, Cat# 10861-558) for 48 hours at 37°C under 80 RPM shaking conditions. Individual wells were inoculated with 106 CFU/mL in 800 µL. After the 48-hour incubation, media was removed, and each well was rinsed with 1 mL of 0.85% saline solution to remove any unattached or lysed cells. This growth protocol was adapted from Fleming et. al (Fleming et al., 2020). The entire plate was covered with parafilm and placed into a water bath sonicator (Fischer Scientific, Cat# 15337411) for 30 minutes. This sonication condition was selected based on previously published biofilm disruption protocols (Fleming, Chahin, & Rumbaugh, 2017; Redman et al., 2021) and was not used independently optimized in the present study. Because the purpose of this work was to evaluate BacQuant as an image-based method for quantifying cells and residual aggregates following a standard disruption step, additional optimization of sonication intensity, during, or enzymatic pretreatment was outside the scope of this study. After sonication, the contents of each well were moved to a respective 1.5 mL microcentrifuge tube.
Slide Preparation and Viability Assay
After harvesting, 10 µL of cell solution was added onto a glass microscope slide (Globe Scientific, Inc, Cat# 1304). The solution was spread into a 1 cm2 area at the center of each slide using an inoculation loop. The samples were then heat-fixed onto the slides and stained with Safranin Advanced Counterstain (Hardy Diagnostics, Cat# GK400) for 1 minute and subsequently rinsed with deionized water. The slides were then wet-mounted using immersion oil and topped with a glass cover slip. Concurrently, the remaining cell solution was serially diluted in saline and drop-plated onto solid LB agar and incubated for 18-24 hours at 37°C for CFU enumeration.
Brightfield Microscopy
All slides were imaged using an Olympus BX43 microscope under oil immersion at 100x magnification. Five representative images of each slide were taken, representing one well in the 24-well plate model. An Olympus DP22 camera was used with CellSens Entry software to take the images. The size of each image and light intensity was constant across all images. The dimensions of each image were measured to be 5000 x 7000 nm, in metric units using the Linear Ruler tool in CellSens Entry. Images were saved and label-matched to their corresponding drop-plate.
Image Analysis
A custom computer vision image analysis pipeline was built using Python v3.8 programming language. A stand-alone version of the pipeline is provided and can be run as an online notebook in or as a script with a Python IDE. All necessary interaction with the code is clearly labeled for the user as comments in the code, as this pipeline is not based on a graphical user interface (GUI). This pipeline should be adapted for individual experiments, as it is currently optimized for use with P. aeruginosa mono-species biofilm cells. All parameters are clearly labeled in the code and are easily customizable. All code is freely available, with the implementation of each step of the pipeline clearly shown. The code is available through Google Colaboratory (https://colab.research.google.com/drive/1JWpmjoXKtgYmeux7MUyBH6syZLHm1inf?usp=sharing). We recommend this pipeline be used with GPU acceleration for high-throughput analyses, but running the process in the cloud or with a CPU is also easily supported. The pipeline takes .png file format images as the primary input, as well as user-specified parameters such as the area of an individual cell in pixels and image size.
The first step of the pipeline entails generating a binary mask of the image based on color threshold values. The mask eliminates background noise in the image and standardizes the shape and sizes of all image features. Following masking, a diameter-based size thresholding algorithm is used to label large connected regions in the image as aggregates in blue, and smaller regions as free cells in yellow. This simplifies the visual distinction between aggregates and free cells. From this color-changing step, the percentages of aggregated biomass and free cells are determined via color-based pixel pooling. For the purpose of this analysis, an aggregate is defined as any connected segmented region with an estimated diameter greater than the user-defined threshold for a single cell. Segmented regions at or below this threshold are classified as free cells. Smaller clusters like doublets, triplets, and quadruplets are classified according to the same size-based rules and were not assigned separate biological categories. If the connected region exceeds the aggregate threshold, it is labeled as an aggregate and its cell number is estimated by dividing the segmented area by the user defined single-cell average area in pixels. If the region does not exceed the threshold, it is classified as a free-cell region.
Concurrently, a contour detection algorithm from the OpenCV (https://github.com/itseez/opencv) library is used to extract aggregated regions of cells as features. The edges of each aggregate are separated from the background image using Canny edge detection (DOI: 10.1109/TPAMI.1986.4767851). Then, the remaining regions are enclosed by a boundary line through contour detection. This allows for the thorough definition of the 2D shape of each aggregate. From these contours, the area of each aggregate is calculated in pixels and converted to square microns. Cell counts within the aggregates are based on the area of an individual cell in pixels, which is defined by the user at the beginning of the pipeline. It is important to note that this process only works on two-dimensional images. It also can be used with epifluorescence microscopy and live/dead staining with modification in the image processing code to exclude dead cells. It is not optimized for Z-stack images, though it can be modified to work with image arrays and high-throughput imaging results. This makes BacQuant a useful tool for more basic laboratories without complex equipment, software, or imaging capabilities.
The output of this pipeline includes the percent aggregated biomass in each image, number of aggregates, estimated number of cells in aggregates, total estimated cells in each image, and estimated calculation of cells per mL of solution. The raw data from this analysis is stored as a .csv file format spreadsheet. We recommend that further processing of the data be conducted in Microsoft Excel.
Statistical Analysis
Statistical analysis was conducted using GraphPad Prism. A paired t-test was utilized to compare ground truth and BacQuant counts of aggregates and free cells. An unpaired t-test was used to compare viable plate count assay results with BacQuant results. All analyses were conducted to determine statistical significance with an alpha value of 0.05.
","reagents":"","patternDescription":"Biofilms are communities of bacteria encased in a matrix composed of extracellular polymeric substances (EPS) including lipids, proteins, extracellular DNA, and polysaccharides (Flemming et al., 2025). These communities are ubiquitous across medical, industrial, and environmental systems and are responsible for an estimated $4 trillion in global economic damage annually (Camara et al., 2022). Clinically, biofilms contribute to approximately 80% of human infections, where the EPS confers protection from antibiotics and host immune responses (Fedorowski, Moller, & Melander, 2013). Both Gram-positive species, such as Staphylococcus aureus, and Gram-negative species, such as Pseudomonas aeruginosa, exhibit biofilm-associated tolerance and persistence that complicate treatment (Schaber et al., 2007; Usui, Yoshii, Thiriet-Rupert, Ghigo, & Beloin, 2023).
A major challenge in biofilm research is accurate quantification of bacterial populations following experimental treatment. Conventional colony-forming unit (CFU) or viable plate counting (VPC) methods remain widely used because they specifically measure culturable, viable bacteria. However, in biofilm samples, VPC may not fully reflect the total number of cells present because aggregates containing multiple cells can produce a single colony if they are not completely freed from the matrix before plating (Beal et al., 2020; Martini, Boddu, Nemenman, & Vega, 2024). Mechanical disruption methods, such as homogenization or sonication, are commonly used to break up biofilms prior to plating. While optimized detachment and sonication protocols can substantially improve biofilm separation, incomplete dissociation of EPS-bound aggregates may still occur depending on the organism, biofilm structure, treatments, and disruption protocol used (Buckingham-Meyer et al., 2022; Korshoj & Kielian, 2024).
Recent advances in computer vision and microscopy offer alternative strategies for microbial quantification. Image-based methods can distinguish individual cells from aggregates using segmentation and contour detection, providing structural information that is lost in culture-based approaches (Holicheva et al., 2025; J. Wang et al., 2022). However, existing tools such as BiofilmQ and confocal-based pipelines are computationally intensive, require specialized hardware, or are not designed for post-disruption enumeration (Hartmann et al., 2021; Mountcastle et al., 2021).
Here, we present BacQuant, a computationally inexpensive image processing pipeline to improve quantification of biofilm cells following sonication using brightfield microscopy. Using P. aeruginosa as a model organism, BacQuant differentiates free cells from aggregates and estimates aggregate cell numbers to improve quantification relative to VPC methods (Figure 1A). It is important to note that BacQuant does not intend to count viable cells. Rather, it estimates the total cell count in the biofilm sample including both live and dead cells. It does not estimate live cells like traditional VPC methods do.
Sonication of P. aeruginosa biofilms consistently yields two morphologies – free individual cells and EPS-encased aggregates (Figure 1B). Operationally, BacQuant defines these categories by segmented object size rather than by visual interpretation alone. Free cells displayed relatively uniform size and shape, whereas aggregates varied widely in morphology, often appearing circular with irregular edges and measuring up to ~1000 µm in diameter. Safranin staining revealed higher color saturation in aggregates, reflecting increased biomass and EPS. Both morphologies were present in all samples, indicating incomplete disaggregation by sonication.
To evaluate BacQuant performance, images were treated as biological replicates and manually annotated to generate ground truth labels. There was a significant difference (p=0.0001; Figure 1C) in the number of free cells identified by ground truth and those identified by BacQuant. This underestimation likely arises from pixel-based normalization to a user-defined average cell area, which does not account for cell orientation or morphological variability.
In contrast, BacQuant slightly overestimated aggregate counts compared to ground truth, although this difference was not statistically significant (p=0.0569; Figure 1D). This trend likely reflects conservative edge detection during segmentation and the lack of depth information in two-dimensional images, which may obscure internal aggregate structure.
BacQuant total cell counts were compared to traditional VPC measurements obtained via serial dilution and drop-plating (Figure 1A,E). Across twenty samples, BacQuant produced significantly higher mean cell counts than VPC (p=0.0001), with averages of 1.18 x 1011 and 4.46 x 107 cells, respectively (Figure 1F). BacQuant results were more tightly clustered, whereas VPC values exhibited greater variability.
This discrepancy is consistent with the presence of biofilm aggregates that contain many cells but yield only a single colony on agar plates. Additionally, BacQuant includes dead cells in its estimates, whereas VPC counts only viable cells, further contributing to higher total counts.
This study introduces BacQuant, a computationally inexpensive image-processing pipeline for quantifying biofilm cells following sonication. BacQuant addresses a fundamental limitation of CFU-based methods by explicitly accounting for residual aggregates that persist after mechanical disruption and confound viable cell estimates (Fleming et al., 2020). By combining thresholding with edge and contour detection, BacQuant enables reproducible instant segmentation of individual cells and aggregates.
Sonication was selected as the representative disruption method due to its widespread use in laboratory, clinical, and industrial contexts. While sonication effectively detaches biofilms from surfaces, it does not reliably dissociate biofilms into single cells, leaving EPS-bound aggregates intact (Kragh et al., 2016) (Figure 1B). These aggregates are indistinguishable from single cells in VPC assays, leading to systematic underestimation of bacterial load. BacQuant circumvents this issue by estimating the number of cells within aggregates, producing significantly higher and more consistent counts than VPC (Figure 1F).
A major strength of BacQuant is its accessibility. Unlike machine learning-based or confocal approaches, the pipeline does not require training data, specialized hardware, or bulky software installations. It can be run on standard CPUs and adapted to diverse imaging conditions, making it broadly applicable across laboratories. The method captures biologically meaningful structure features, specifically the distinction between free cells and aggregates, that are routinely missed by culture-based techniques.
Nevertheless, BacQuant has limitations. In dense images, over-segmentation may occur, leading to slight overestimation of aggregate numbers (Figure 1D). Conversely, free cells are underestimated due to reliance on an average cell area parameter that does not account for morphological variability (Figure 1C). The pipeline also cannot distinguish between live and dead cells, which may inflate estimates relative to viable counts. If cells occur in small clusters like doublets or triplets, it is processed as an aggregate while they could be a few individual cells in close promixity to one another. Another major limitation is the imposition of 2D structure on the 3D aggregate morphology. Because BacQuant only uses two-dimensional estimation, edge detection is conservative and does not take the height of the structure into account when determining cell density within large cell clusters, dampening the true estimate of cell density within. Additionally, accurate performance depends on high-contrast microscopy images and correct calibration for each experimental setup.
Despite these constraints, BacQuant provides a rapid and scalable complement to traditional microbiological methods. The entire analysis can be completed within minutes, compared to hours or days required for plating assays. Accurate biofilm quantification is critical for antimicrobial testing, clinical research, and environmental monitoring, and BacQuant offers a practical tool for improving structural enumeration of bacterial populations (Folliero et al., 2021).
Future work should integrate live/dead staining, molecular viability markers, and three-dimension imaging to refine estimates of active biofilm populations (Sauer et al., 2022; Wang, Zhu, Zheng, Dong, & Liu, 2022). Incorporation of machine learning-based segmentation could further enhance classification of complex morphologies. Ultimately, BacQuant provides a foundation for automated, image-based biofilm quantification and highlights the value of computer vision approaches in microbiology research..
","references":[{"reference":"Acosta N, Waddell B, Heirali A, Somayaji R, Surette MG, Workentine ML, Rabin HR, Parkins MD. 2020. Cystic Fibrosis Patients Infected With Epidemic Pseudomonas aeruginosa Strains Have Unique Microbial Communities. Frontiers in Cellular and Infection Microbiology 10: 10.3389/fcimb.2020.00173.
","pubmedId":"","doi":"10.3389/fcimb.2020.00173"},{"reference":"Beal J, Farny NG, Haddock-Angelli T, Selvarajah V, Baldwin GS, Buckley-Taylor R, et al., Zhou. 2020. Robust estimation of bacterial cell count from optical density. Communications Biology 3: 10.1038/s42003-020-01127-5.
","pubmedId":"","doi":"10.1038/s42003-020-01127-5"},{"reference":"Bjerkan G, Witsø E, Bergh Kr. 2009. Sonication is superior to scraping for retrieval of bacteria in biofilm on titanium and steel surfaces in vitro. Acta Orthopaedica 80: 245-250.
","pubmedId":"","doi":"10.3109/17453670902947457"},{"reference":"Buckingham-Meyer K, Miller LA, Parker AE, Walker DK, Sturman P, Novak I, Goeres DM. 2022. Harvesting and Disaggregation: An Overlooked Step in Biofilm Methods Research. Journal of Visualized Experiments : 10.3791/62390.
","pubmedId":"","doi":"10.3791/62390"},{"reference":"Cámara M, Green W, MacPhee CE, Rakowska PD, Raval R, Richardson MC, et al., Webb. 2022. Economic significance of biofilms: a multidisciplinary and cross-sectoral challenge. npj Biofilms and Microbiomes 8: 10.1038/s41522-022-00306-y.
","pubmedId":"","doi":"10.1038/s41522-022-00306-y"},{"reference":"Fedorowski A, Möller SJ, Melander O. 2013. Response to the letter by prof.
Fleming D, Chahin L, Rumbaugh K. 2017. Glycoside Hydrolases Degrade Polymicrobial Bacterial Biofilms in Wounds. Antimicrobial Agents and Chemotherapy 61: 10.1128/aac.01998-16.
","pubmedId":"27872074","doi":"10.1128/AAC.01998-16 "},{"reference":"Fleming D, Redman W, Welch GS, Mdluli NV, Rouchon CN, Frank KL, Rumbaugh KP. 2020. Utilizing glycoside hydrolases to improve the quantitation and visualization of biofilm bacteria. Biofilm 2: 100037.
","pubmedId":"","doi":"10.1016/j.bioflm.2020.100037"},{"reference":"Flemming HC, van Hullebusch ED, Little BJ, Neu TR, Nielsen PH, Seviour T, et al., Wuertz. 2024. Microbial extracellular polymeric substances in the environment, technology and medicine. Nature Reviews Microbiology 23: 87-105.
","pubmedId":"","doi":"10.1038/s41579-024-01098-y"},{"reference":"Folliero V, Franci G, Dell’Annunziata F, Giugliano R, Foglia F, Sperlongano R, et al., Galdiero. 2021. Evaluation of Antibiotic Resistance and Biofilm Production among Clinical Strain Isolated from Medical Devices. International Journal of Microbiology 2021: 1-11.
","pubmedId":"","doi":"10.1155/2021/9033278"},{"reference":"Fowler TE, Bloomquist RF, Sakhalkar MV, Bloomquist DT. 2023. Chronic Purulent Conjunctivitis Associated With Extensively Drug-Resistant Pseudomonas aeruginosa. JAMA Ophthalmology 141: 609.
","pubmedId":"","doi":"10.1001/jamaophthalmol.2023.1529"},{"reference":"Goldufsky J, Wood SJ, Jayaraman V, Majdobeh O, Chen L, Qin S, et al., Shafikhani. 2015. Pseudomonas aeruginosa uses T3SS to inhibit diabetic wound healing. Wound Repair and Regeneration 23: 557-564.
","pubmedId":"","doi":"10.1111/wrr.12310"},{"reference":"Gonzalez MR, Fleuchot B, Lauciello L, Jafari P, Applegate LA, Raffoul W, Que YA, Perron K. 2016. Effect of Human Burn Wound Exudate on Pseudomonas aeruginosa Virulence. mSphere 1: 10.1128/msphere.00111-15.
","pubmedId":"","doi":"10.1128/mSphere.00111-15"},{"reference":"Hartmann R, Jeckel H, Jelli E, Singh PK, Vaidya S, Bayer M, et al., Drescher. 2021. Quantitative image analysis of microbial communities with BiofilmQ. Nature Microbiology 6: 151-156.
","pubmedId":"","doi":"10.1038/s41564-020-00817-4"},{"reference":"Holicheva AA, Kozlov KS, Boiko DA, Kamanin MS, Provotorova DV, Kolomoets NI, Ananikov VP. 2025. Deep generative modeling of annotated bacterial biofilm images. npj Biofilms and Microbiomes 11: 10.1038/s41522-025-00647-4.
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