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arXiv · 2602.11628

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

Yeva Gabrielyan·Varduhi Yeghiazaryan·Irina Voiculescu
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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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Yeva Gabrielyan, Varduhi Yeghiazaryan, Irina Voiculescu. 2026-02-12. PLESS: Pseudo-Label Enhancement with Spreading Scribbles for Weakly Supervised Segmentation. https://arxiv.org/abs/2602.11628

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