An Effective Deep Learning Framework for Cell Segmentation in Microscopy Images.

Abstract

Cell segmentation is a common step in cell behavior analysis. Reliably and automatically segmenting cells in microscopy images remains challenging, especially in differential inference contrast microscopy images and phase-contrast microscopy images. In this paper, we propose a deep learning solution combining a Mask RCNN architecture with Shape-Aware Loss to produce cell instance segmentation. Our approach outperforms prior works in cell segmentation, achieving an IOU of 91.91% on the DIC-C2DH-HeLa dataset and an IOU of 94.93 % on the PhC-C2DH-U373 dataset. Our framework can calculate cell instance segmentation masks from both types of microscopy images without any additional post-processing.Clinical Relevance - The proposed approach produces accurate instance segmentation in Differential Inference Contrast and Phase-Contrast microscopy images. The segmentation results can be reliably used in cell behavior analysis and cell tracking.