Inferring the location and orientation of cell divisions on time-lapse image sequences

Abstract

We propose a two-stage method to characterize cell divisions. In a first stage, the division detection problem is recast into a semantic segmentation task on image sequences. In a second stage, a local regression on individual divisions yields the orientation and distance between daughter cells. We apply our formalism to confocal image sequences of neural tube formation in chicken embryos, where divisions occur within a well-defined plane. We show that our two-stage method can be implemented using simple networks, e.g. a U-Net for the segmentation and a 4-layer CNN for the regression. Optimization of the networks was achieved through a systematic exploration of hyperparameters. In particular, we show that considering several frames as inputs significantly improves the segmentation performance. We reach a performance of 96% in the F1 measure for the detection and errors for the angle, which are within the bounds of the uncertainty of the ground-truth annotation dataset.