Automatic detection of cell divisions (mitosis) in live-imaging microscopy images using Convolutional Neural Networks

We propose a semi-automated pipeline for the detection of possible cell divisions in live-imaging microscopy and the classification of these mitosis candidates using a Convolutional Neural Network (CNN). We use time-lapse images of NIH3T3 scratch assay cultures, extract patches around bright candidate regions that then undergo segmentation and binarization, followed by a classification of the binary patches into either containing or not containing cell division. The classification is performed by training a Convolutional Neural Network on a specially constructed database. We show strong results of AUC = 0.91 and F-score = 0.89, competitive with state-of-the-art methods in this field.