Cross comparison representation learning for semi-supervised segmentation of cellular nuclei in immunofluorescence staining

Jianran Ren, Jingyi Che, Peicong Gong, Xiaojun Wang, Xiangning Li, Anan Li, Chi Xiao

Abstract: Highlights•We proposed a semi supervised learning framework based on cross comparison representation learning for the first time. This framework enhances the segmentation ability of the network by comparing learning algorithms to enhance the feature fitting before the teacher-student model in a semi-supervised learning framework.•We also provided a public ISC dataset for facilitating the semi-supervised cell segmentation research, which contains 2550 immunofluorescence staining images and human-labeled ground truth.•We tested the proposed method on the ISC and CRAG datasets. It achieves average Jaccard, Dice and Normalized Surface Dice (NSD) indicators of 83.22%, 90.95% and 81.90% with only 20% labeled data on ISC and CRAG datasets, which surpasses other state-of-the-art semi-supervised learning methods.