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

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 .