用涂鸦传播增强伪标签，提升弱监督分割精度
PLESS: Pseudo-Label Enhancement with Spreading Scribbles for Weakly Supervised Segmentation
- 通过层次化区域划分，将涂鸦信息传播到语义一致区域
- 在两个心脏MRI数据集上，分割准确率显著提升
- 通用框架，可无缝集成到现有伪标签方法中
弱监督学习中的涂鸦标注仅需用户绘制少量笔画即可指示部分像素的分割标签，大幅降低密集像素标注成本，但存在噪声大、不完整的问题。现有医学图像分割方法虽采用伪标签训练缓解此问题，但伪标签质量仍是性能瓶颈。本文提出PLESS，一种通用的伪标签增强策略，通过将图像分层划分为空间连贯区域，将涂鸦信息传播至语义一致区域以优化伪标签，提升其可靠性和空间一致性。该框架与模型无关，可轻松集成至现有伪标签方法。在两个公开心脏MRI数据集（ACDC、MSCMRseg）上，对四种涂鸦监督算法的实验均显示分割精度持续提升。代码将在录用后开源。
原文摘要 · Abstract (English)
Weakly supervised learning with scribble annotations uses sparse user-drawn strokes to indicate segmentation labels on a small subset of pixels. This annotation reduces the cost of dense pixel-wise labeling, but suffers inherently from noisy and incomplete supervision. Recent scribble-based approaches in medical image segmentation address this limitation using pseudo-label-based training; however, the quality of the pseudo-labels remains a key performance limit. We propose PLESS, a generic pseudo-label enhancement strategy which improves reliability and spatial consistency. It builds on a hierarchical partitioning of the image into a hierarchy of spatially coherent regions. PLESS propagates scribble information to refine pseudo-labels within semantically coherent regions. The framework is model-agnostic and easily integrates into existing pseudo-label methods. Experiments on two public cardiac MRI datasets (ACDC and MSCMRseg) across four scribble-supervised algorithms show consistent improvements in segmentation accuracy. Code will be made available on GitHub upon acceptance.
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