PLESS: Pseudo-Label Enhancement with Spreading Scribbles for Weakly Supervised Segmentation

• URL: http://arxiv.org/abs/2602.11628v1
• Date: Thu, 12 Feb 2026 06:24:05 GMT
• Title: PLESS: Pseudo-Label Enhancement with Spreading Scribbles for Weakly Supervised Segmentation
• Authors: Yeva Gabrielyan, Varduhi Yeghiazaryan, Irina Voiculescu,
• Abstract summary: Weakly supervised learning with scribble annotations uses sparse user-drawn strokes to indicate segmentation labels on a small subset of pixels.<n>Recent scribble-based approaches in medical image segmentation address this limitation using pseudo-label-based training.<n>We propose PLESS, a generic pseudo-label enhancement strategy which improves reliability and spatial consistency.
• Score: 5.862480696321742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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