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

• URL: http://arxiv.org/abs/2601.17366v1
• Date: Sat, 24 Jan 2026 08:21:14 GMT
• Title: UCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation
• Authors: Chengbo Ding, Fenghe Tang, Shaohua Kevin Zhou,
• Abstract summary: UCAD is an Uncertainty-Guided Contour-Aware Displacement framework for semi-supervised medical image segmentation.<n>Our framework preserves contour-aware semantics while enhancing consistency learning.<n>Experiments demonstrate that UCAD consistently outperforms state-of-the-art semi-supervised segmentation methods.
• Score: 0.9983209881417459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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