title: Automatic detection of cell divisions (mitosis) in live-imaging microscopy images using Convolutional Neural Networks.
abstract: 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.
authors.forename: Anat
authors.initials: A
authors.lastname: Shkolyar
authors.forename: Amit
authors.initials: A
authors.lastname: Gefen
authors.forename: Dafna
authors.initials: D
authors.lastname: Benayahu
authors.forename: Hayit
authors.initials: H
authors.lastname: Greenspan
mesh_terms: D000818:Animals; D051379:Mice; D008853:Microscopy; D008938:Mitosis; D041681:NIH 3T3 Cells; D016571:Neural Networks, Computer