BacQuant: A Scalable Automated Image Processing Pipeline for Quantifying Biofilm Aggregates

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.