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

Area of Science:

• Microbiology
• Biofilm research
• Computational biology

Background:

• Traditional microbiology quantifies bacteria using viable plate counts.
• Plate counts often underestimate bacterial biofilm density due to cell aggregation within the matrix.
• Accurate biofilm quantification is crucial for understanding microbial communities and developing treatments.

Purpose of the Study:

• To develop and validate BacQuant, a computer vision pipeline for accurate bacterial biofilm quantification.
• To compare the performance of BacQuant against traditional viable plate counting.
• To provide a scalable and cost-effective method for biofilm analysis.

Main Methods:

• Developed a Python and OpenCV-based computer vision pipeline named BacQuant.
• Utilized image thresholding, segmentation, and contour detection on brightfield microscopy images.
• Compared BacQuant's automated cell counts with manual microscopy counts and viable plate counts.

Main Results:

• BacQuant successfully distinguished individual bacterial cells from aggregates within biofilms.
• Automated cell counts from BacQuant closely matched manual microscopy counts.
• BacQuant produced higher estimated bacterial densities compared to viable plate counting, indicating a more comprehensive assessment.

Conclusions:

• BacQuant offers a scalable, inexpensive, and complementary method for biofilm quantification.
• The computer vision approach provides a more accurate estimation of total cell burden in biofilms.
• BacQuant enhances traditional microbiological techniques for biofilm research.