2021/03/09 by Tanguy Naets, Maarten Huijsmans, Naets, Tanguy +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Environmental Science · #Bacteriophages and microbial interactions #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Biological sciences #FOS: Computer and information sciences #Image Processing Techniques and Applications #Machine Learning (cs.LG) #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2103.05337
openalex publication_date 2021/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present an application of the well-known Mask R-CNN approach to the counting of different types of bacterial colony forming units that were cultured in Petri dishes. Our model was made available to lab technicians in a modern SPA (Single-Page Application). Users can upload images of dishes, after which the Mask R-CNN model that was trained and tuned specifically for this task detects the number of BVG- and BVG+ colonies and displays these in an interactive interface for the user to verify. Users can then check the model's predictions, correct them if deemed necessary, and finally validate them. Our adapted Mask R-CNN model achieves a mean average precision (mAP) of 94% at an intersection-over-union (IoU) threshold of 50%. With these encouraging results, we see opportunities to bring the benefits of improved accuracy and time saved to related problems, such as generalising to other bacteria types and viral foci counting.