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bioRxiv · 10.1101/2022.05.11.491558

Pytri: A multi-weight detection system for biological entities

Mehdi, A.·Vu, K.-N.·Lambert, F.
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Abstract

The enumeration of biological entities is a critical part of experimental assays and usually requires large lengths of time. The standard method is to count the entities by hand or with OpenCV-based software, which can lead to inaccurate results. Here, we propose an online platform for biologists consisting of a system with multiple trained machine learning weights to detect various biological entities such as yeast colonies, bacterial colonies, and melanoma clusters. The Pytri model achieved a median relative error rate of 7.56% for bacterial and yeast colonies on Petri dishes, 6.58% for colonies on 96-well plates and 10.28% for melanoma cluster microscopy images. We showcase the application of state-of-the-art deep learning tools in bacterial entity detection, achieving significantly higher accuracy than traditional methods when compared to our base standard manual count.

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Mehdi, A., Vu, K.-N., Lambert, F.. 2022-05-12. Pytri: A multi-weight detection system for biological entities. https://doi.org/10.1101/2022.05.11.491558

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