  Nuclei segmentation is a crucial task for whole slide image analysis in
digital pathology. Generally, the segmentation performance of fully-supervised
learning heavily depends on the amount and quality of the annotated data.
However, it is time-consuming and expensive for professional pathologists to
provide accurate pixel-level ground truth, while it is much easier to get
coarse labels such as point annotations. In this paper, we propose a
weakly-supervised learning method for nuclei segmentation that only requires
point annotations for training. First, coarse pixel-level labels are derived
from the point annotations based on the Voronoi diagram and the k-means
clustering method to avoid overfitting. Second, a co-training strategy with an
exponential moving average method is designed to refine the incomplete
supervision of the coarse labels. Third, a self-supervised visual
representation learning method is tailored for nuclei segmentation of pathology
images that transforms the hematoxylin component images into the H&E stained
images to gain better understanding of the relationship between the nuclei and
cytoplasm. We comprehensively evaluate the proposed method using two public
datasets. Both visual and quantitative results demonstrate the superiority of
our method to the state-of-the-art methods, and its competitive performance
compared to the fully-supervised methods. Code:
https://github.com/hust-linyi/SC-Net
