Abstract
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.
Abstract (translated)
带有潦草标注的弱监督学习使用稀疏的手绘笔画来指示少量像素的分割标签，从而减少了密集像素级标记的成本。然而，这种注释本身存在噪声和不完整指导的问题。最近基于潦草标注的方法在医学图像分割中通过伪标签训练解决了这一限制；但是，伪标签的质量仍然是性能的关键瓶颈。 我们提出了PLESS（Pseudo-Label Enhancement Strategy for Scribble Annotations），这是一种通用的伪标签增强策略，旨在提高可靠性和空间一致性。该方法建立在一个将图像划分为一系列空间连贯区域的层次结构上，并利用潦草信息在语义一致区域内细化伪标签。PLESS框架与模型无关且易于整合到现有的伪标签方法中。 实验表明，在两个公开的心脏MRI数据集（ACDC和MSCMRseg）以及四种基于潦草标注的方法上，该策略均显著提高了分割精度。论文被接受后，代码将在GitHub上发布。
URL
https://arxiv.org/abs/2602.11628
