Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance
  Segmentation
Ye Zhang Ziyue Wang Yifeng Wang Hao Bian Linghan Cai Hengrui Li Lingbo Zhang Yongbing Zhang
Semi-supervised segmentation methods have demonstrated promising results in
natural scenarios, providing a solution to reduce dependency on manual
annotation. However, these methods face significant challenges when directly
applied to pathological images due to the subtle color differences between
nuclei and tissues, as well as the significant morphological variations among
nuclei. Consequently, the generated pseudo-labels often contain much noise,
especially at the nuclei boundaries. To address the above problem, this paper
proposes a boundary-aware contrastive learning network to denoise the boundary
noise in a semi-supervised nuclei segmentation task. The model has two key
designs: a low-resolution denoising (LRD) module and a cross-RoI contrastive
learning (CRC) module. The LRD improves the smoothness of the nuclei boundary
by pseudo-labels denoising, and the CRC enhances the discrimination between
foreground and background by boundary feature contrastive learning. We conduct
extensive experiments to demonstrate the superiority of our proposed method
over existing semi-supervised instance segmentation methods.