Ensemble Processing and Synthetic Image Generation For Abnormally Shaped Nuclei Segmentation

Abstract

Abnormalities in biological cell nuclei morphology are correlated with cell cycle stages, disease states, and various external stimuli. There have been many deep learning approaches that have described nuclei segmentation and analysis of nuclear morphology. One problem with many deep learning methods is acquiring large amounts of annotated nuclei data, which is generally expensive to obtain. In this paper, we propose a system to segment abnormally shaped nuclei with a limited amount of training data. We first generate specific shapes of synthetic nuclei groundtruth. We randomly sample these synthetic groundtruth images into training sets to train several Mask R-CNNs. We design an ensemble strategy to combine or fuse segmentation results from the Mask R-CNNs. We also design an oval nuclei removal by StarDist to reduce the false positives and improve the overall segmentation performance. Our experiments indicate that our method outperforms other methods in segmenting abnormally shaped nuclei.