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arXiv · 2601.17366

UCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation

Chengbo Ding·Fenghe Tang·Shaohua Kevin Zhou
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Abstract

Existing displacement strategies in semi-supervised segmentation only operate on rectangular regions, ignoring anatomical structures and resulting in boundary distortions and semantic inconsistency. To address these issues, we propose UCAD, an Uncertainty-Guided Contour-Aware Displacement framework for semi-supervised medical image segmentation that preserves contour-aware semantics while enhancing consistency learning. Our UCAD leverages superpixels to generate anatomically coherent regions aligned with anatomy boundaries, and an uncertainty-guided selection mechanism to selectively displace challenging regions for better consistency learning. We further propose a dynamic uncertainty-weighted consistency loss, which adaptively stabilizes training and effectively regularizes the model on unlabeled regions. Extensive experiments demonstrate that UCAD consistently outperforms state-of-the-art semi-supervised segmentation methods, achieving superior segmentation accuracy under limited annotation. The code is available at:https://github.com/dcb937/UCAD.

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Chengbo Ding, Fenghe Tang, Shaohua Kevin Zhou. 2026-01-24. UCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation. https://arxiv.org/abs/2601.17366

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