BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels
Yi Lin Zeyu Wang Dong Zhang Kwang-Ting Cheng Hao Chen
Nuclei segmentation is a fundamental prerequisite in the digital pathology
workflow. The development of automated methods for nuclei segmentation enables
quantitative analysis of the wide existence and large variances in nuclei
morphometry in histopathology images. However, manual annotation of tens of
thousands of nuclei is tedious and time-consuming, which requires significant
amount of human effort and domain-specific expertise. To alleviate this
problem, in this paper, we propose a weakly-supervised nuclei segmentation
method that only requires partial point labels of nuclei. Specifically, we
propose a novel boundary mining framework for nuclei segmentation, named BoNuS,
which simultaneously learns nuclei interior and boundary information from the
point labels. To achieve this goal, we propose a novel boundary mining loss,
which guides the model to learn the boundary information by exploring the
pairwise pixel affinity in a multiple-instance learning manner. Then, we
consider a more challenging problem, i.e., partial point label, where we
propose a nuclei detection module with curriculum learning to detect the
missing nuclei with prior morphological knowledge. The proposed method is
validated on three public datasets, MoNuSeg, CPM, and CoNIC datasets.
Experimental results demonstrate the superior performance of our method to the
state-of-the-art weakly-supervised nuclei segmentation methods. Code:
https://github.com/hust-linyi/bonus.