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.

翻译：半监督分割方法在自然场景中展现出令人期待的效果，为减少对人工标注的依赖提供了解决方案。然而，由于细胞核与组织间微弱的颜色差异以及细胞核间显著的形态变异，这些方法直接应用于病理图像时面临重大挑战。由此生成的伪标签常包含大量噪声，尤其在细胞核边界区域。针对上述问题，本文提出一种面向边界的对比学习网络，用于在半监督细胞核分割任务中抑制边界噪声。该模型包含两个关键设计：低分辨率去噪模块和跨区域对比学习模块。低分辨率去噪模块通过伪标签去噪提升细胞核边界平滑度，跨区域对比学习模块则通过边界特征对比学习增强前景与背景的判别性。通过大量实验证明，本方法在现有半监督实例分割方法中具有显著优越性。