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.

翻译：基于涂鸦标注的弱监督学习利用稀疏的用户绘制笔划来指示少量像素子集的分割标签。这种标注方式降低了密集像素级标注的成本，但本质上受到噪声和不完整监督的影响。近期医学图像分割中基于涂鸦的方法通过伪标签训练来解决这一限制；然而，伪标签的质量仍然是关键的性能瓶颈。我们提出PLESS，一种通用的伪标签增强策略，旨在提升伪标签的可靠性与空间一致性。该方法基于将图像层次化分割为空间连贯区域的多级结构。PLESS通过传播涂鸦信息，在语义连贯区域内优化伪标签。该框架与模型无关，可轻松集成到现有伪标签方法中。在两个公开心脏MRI数据集（ACDC和MSCMRseg）上，对四种涂鸦监督算法进行的实验表明，该方法在分割精度上取得了持续提升。代码将在论文录用后于GitHub开源。