UCAD: 基于不确定性的轮廓感知位移用于半监督医学图像分割
UCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation
AI总结：
UCAD通过基于不确定性的轮廓感知位移框架，提升半监督医学图像分割的准确性和一致性学习效果。
摘要：
现有半监督分割中的位移策略仅在矩形区域上操作，忽略了解剖结构，导致边界扭曲和语义不一致。为了解决这些问题，我们提出UCAD，一种基于不确定性的轮廓感知位移框架，用于半监督医学图像分割，以保留轮廓感知语义并增强一致性学习。我们的UCAD利用超像素生成与解剖边界对齐的解剖学一致区域，并采用基于不确定性的选择机制，选择性地位移具有挑战性的区域以获得更好的一致性学习。我们进一步提出了动态不确定权重一致性损失，该损失能够自适应地稳定训练并在未标记区域上有效正则化模型。大量实验表明，UCAD在有限标注下一致优于最先进的半监督分割方法，实现了更高的分割准确性。代码可在：https://github.com/dcb937/UCAD获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
