Weakly-supervised segmentation (WSS) has emerged as a solution to mitigate the conflict between annotation cost and model performance by adopting sparse annotation formats (e.g., point, scribble, block, etc.). Typical approaches attempt to exploit anatomy and topology priors to directly expand sparse annotations into pseudo-labels. However, due to a lack of attention to the ambiguous edges in medical images and insufficient exploration of sparse supervision, existing approaches tend to generate erroneous and overconfident pseudo proposals in noisy regions, leading to cumulative model error and performance degradation. In this work, we propose a novel WSS approach, named ProCNS, encompassing two synergistic modules devised with the principles of progressive prototype calibration and noise suppression. Specifically, we design a Prototype-based Regional Spatial Affinity (PRSA) loss to maximize the pair-wise affinities between spatial and semantic elements, providing our model of interest with more reliable guidance. The affinities are derived from the input images and the prototype-refined predictions. Meanwhile, we propose an Adaptive Noise Perception and Masking (ANPM) module to obtain more enriched and representative prototype representations, which adaptively identifies and masks noisy regions within the pseudo proposals, reducing potential erroneous interference during prototype computation. Furthermore, we generate specialized soft pseudo-labels for the noisy regions identified by ANPM, providing supplementary supervision. Extensive experiments on three medical image segmentation tasks involving different modalities demonstrate that the proposed framework significantly outperforms representative state-of-the-art methods

翻译：弱监督分割（WSS）通过采用稀疏标注格式（如点、涂鸦、块等）来缓解标注成本与模型性能之间的矛盾。典型方法试图利用解剖和拓扑先验直接将稀疏标注扩展为伪标签。然而，由于缺乏对医学图像中模糊边界的关注以及对稀疏监督的探索不足，现有方法倾向于在噪声区域生成错误且过度自信的伪建议，导致模型误差累积和性能下降。本文提出一种名为ProCNS的新型弱监督分割方法，包含两个协同模块，其设计遵循渐进式原型校准与噪声抑制原则。具体而言，我们设计了基于原型的区域空间亲和（PRSA）损失，以最大化空间与语义元素之间的成对亲和度，为目标模型提供更可靠的引导。该亲和度源自输入图像和经过原型优化的预测结果。同时，我们提出了自适应噪声感知与掩码（ANPM）模块，以获取更丰富且更具代表性的原型表征，该模块能够自适应地识别并掩码伪建议中的噪声区域，减少原型计算过程中的潜在错误干扰。此外，我们为ANPM识别的噪声区域生成专门的软伪标签，提供补充监督。在涉及不同模态的三项医学图像分割任务上的大量实验表明，所提出的框架显著优于代表性先进方法。