Segment Membranes and Nuclei from Histopathological Images via Nuclei Point-Level Supervision

Hansheng Li, Zhengyang Xu, Mo Zhou, Xiaoshuang Shi, Yuxin Kang, Qirong Bu, Hong Lv, Ming Li, Mingzhen Lin, Lei Cui, Jun Feng, Wentao Yang, Lin Yang

Abstract: Accurate segmentation and analysis of membranes from immunohistochemical (IHC) images are crucial for cancer diagnosis and prognosis. Although several fully-supervised deep learning methods for membrane segmentation from IHC images have been proposed recently, the high demand for pixel-level annotations makes this process time-consuming and labor-intensive. To overcome this issue, we propose a novel deep framework for membrane segmentation that utilizes nuclei point-level supervision. Our framework consists of two networks: a Seg-Net that generates segmentation results for membranes and nuclei, and a Tran-Net that transforms the segmentation into semantic points. In this way, the accuracy of the semantic points is closely related to the segmentation quality. Thus, the inconsistency between the semantic points and the point annotations can be used as effective supervision for cell segmentation. We evaluated the proposed method on two IHC membrane-stained datasets and achieved an 81.36% IoU and 85.51% \(F_1\) score of the fully supervised method. All source codes are available here.