Dynamic Pseudo Label Optimization in Point-Supervised Nuclei Segmentation

TL;DR

This paper introduces DoNuSeg, a framework that dynamically optimizes pseudo labels for nuclei segmentation using class activation maps and contrastive learning, significantly improving performance in point-supervised settings.

Contribution

The paper proposes a novel dynamic pseudo label optimization framework leveraging CAMs and contrastive modules for improved nuclei segmentation with point supervision.

Findings

Outperforms state-of-the-art point-supervised methods

Effective use of CAMs for adaptive pseudo mask generation

Enhanced segmentation accuracy through contrastive learning

Abstract

Deep learning has achieved impressive results in nuclei segmentation, but the massive requirement for pixel-wise labels remains a significant challenge. To alleviate the annotation burden, existing methods generate pseudo masks for model training using point labels. However, the generated masks are inevitably different from the ground truth, and these dissimilarities are not handled reasonably during the network training, resulting in the subpar performance of the segmentation model. To tackle this issue, we propose a framework named DoNuSeg, enabling \textbf{D}ynamic pseudo label \textbf{O}ptimization in point-supervised \textbf{Nu}clei \textbf{Seg}mentation. Specifically, DoNuSeg takes advantage of class activation maps (CAMs) to adaptively capture regions with semantics similar to annotated points. To leverage semantic diversity in the hierarchical feature levels, we design a dynamic selection module to choose the optimal one among CAMs from different encoder blocks as pseudo masks. Meanwhile, a CAM-guided contrastive module is proposed to further enhance the accuracy of pseudo masks. In addition to exploiting the semantic information provided by CAMs, we consider location priors inherent to point labels, developing a task-decoupled structure for effectively differentiating nuclei. Extensive experiments demonstrate that DoNuSeg outperforms state-of-the-art point-supervised methods. The code is available at https://github.com/shinning0821/MICCAI24-DoNuSeg.