Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells by Deep Learning for Cell Center Detection using Three-Dimensional Confocal Fluorescence Microscopy Images.

Abstract

Accurate single cell segmentation provides means to monitor the behavior of single cell within a population of cells. Time-lapse fluorescence images are used to reveal heterogeneous nature of single mouse embryonic stem cell (ESC) colony and monitor fluctuations of the cell states. Automatic quantification of speed and status shifts of the ESCs depends on accurate single cell segmentation that is used to calculate the 3D center of every cell and track this cell for the quantification. This study proposes a new 3D U-net to accurately detect center of each single cell in 3D confocal images. The dimension of input 3D images to the U-net is flexible so that multiple center detections from different image directions can be implemented simultaneously to improve the center detection accuracy. This study showed that our method can improve accuracy for cell center detection and thus the quantification for ESC speeds and status shifts.