PLESS: 基于扩散涂鸦的伪标签增强用于弱监督分割
PLESS: Pseudo-Label Enhancement with Spreading Scribbles for Weakly Supervised Segmentation
AI总结：
PLESS通过扩散涂鸦信息提升伪标签可靠性，改进弱监督分割性能。
摘要：
弱监督学习中的涂鸦标注使用稀疏的手绘线条来指示分割标签在少量像素上。这种标注减少了密集像素级标注的成本，但固有地面临噪声和不完整监督的问题。最近的基于涂鸦的医学图像分割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性能的关键限制。我们提出了PLESS，一种通用的伪标签增强策略，它提高了可靠性和空间一致性。它基于将图像分层划分成一系列空间上连贯的区域。PLESS将涂鸦信息传播到语义上连贯的区域内以优化伪标签。该框架是模型无关的，可以轻松整合到现有的伪标签方法中。在两个公开的心脏MRI数据集（ACDC和MSCMRseg）上，针对四种基于涂鸦的监督算法进行实验，显示出分割准确性的一致改进。接受后将在GitHub上提供代码。
英文摘要：
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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