A Mask R-CNN approach to counting bacterial colony forming units in pharmaceutical development

Autor:	Naets, Tanguy, Huijsmans, Maarten, Smyth, Paul, Sorber, Laurent, de Lannoy, Gaël
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Quantitative Biology - Quantitative Methods
Druh dokumentu:	Working Paper
Popis:	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. Comment: 9 pages, 3 pdf figures. Extended version of poster presented at ESANN 2020 (European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.05337 View this record from Arxiv