Abstract

Info

Title:: Nuclei segmentation using contrastive learning and contour guided U-Net

Author(s):: JIA Wei1; JIANG Haifeng2; ZHAO Xuefen3; 1. School of Information Engineering, Ningxia University, Yinchuan 750021, China 2. Department of Pathology, General Hospital of Ningxia Medical University, Yinchuan 750021, China 3. Xinhua College, Ningxia University, Yinchuan 750021, China

Keywords:: Keywords: contrastive learning contour feature U-Net nuclei segmentation active contour

Abstract:: Abstract: Aiming at the problems of nucleus edge blurring and overlap in nucleus segmentation, a nuclei segmentation method based on contrastive learning and contour guided U-Net is proposed. The global and local feature contrastive learning is adopted for contrastive learning using the global and local features of unlabeled pathological images, thereby solving the problem of insufficient labeled data. Then a contour guided U-Net is developed to fuse the contour features of each layer of encoder for improving the segmentation performance of U-Net, and the fused contour features and Jeffreys divergence-driven active contour method are used to guide and assist the nuclei segmentation. Finally, the alternate training using the global and local feature contrastive learning and the contour guided U-Net is carried out. The unlabeled data with high confidence are taken as pseudo-label and mixed into labeled data, and the contour guided U-Net is used for segmentation training. Experimental results show that the proposed segmentation method can effectively improve the accuracy of nuclei segmentation, and has a relatively stable segmentation performance.