Pytri: A multi-weight detection system for biological entities

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.