ArticlemicroPublication biology2026
BacQuant: A Scalable Automated Image Processing Pipeline for Quantifying Biofilm Aggregates.
Article in microPublication biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.