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The paper introduces PLESS, a pseudo-label enhancement strategy for weakly supervised segmentation using scribble annotations, addressing the limitations of noisy and incomplete supervision. PLESS leverages a hierarchical partitioning of the image into spatially coherent regions to propagate scribble information and refine pseudo-labels within these regions. Experiments on cardiac MRI datasets demonstrate that PLESS consistently improves segmentation accuracy across different scribble-supervised algorithms.
Stop letting noisy pseudo-labels limit your weakly supervised segmentation: PLESS propagates scribble annotations within semantically coherent regions to boost accuracy.
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.
