用解剖先验和不确定性感知提升主动脉瘤血栓分割准确率
Trust the Prior (or Not): Uncertainty-Aware Abdominal Aortic Aneurysm Segmentation
- 基于局部解剖的患者特异性强度归一化，增强图像一致性
- 引入置信度自适应的解剖注意力模块，提升模糊区域分割精度
- 在多中心数据上表现更优，结果可解释性强，适合医学影像研究
主动脉瘤内血栓的精准分割对风险评估至关重要，但因血栓特征异质且与未增强组织对比度低，加之不同CTA扫描协议导致的数据域偏移，使深度学习模型跨中心泛化困难。为此，提出一种患者特异性框架，融合判别性学习与解剖先验。方法包含两项关键设计：(1) 基于局部解剖高斯混合模型的患者特异性强度归一化；(2) 不确定性门控解剖注意力模块，结合空间先验并根据体素置信度自适应调节其影响。该设计在模糊区域提供解剖引导，同时抑制不可靠先验。所提方法在分布内测试中达到最先进性能，在外部多中心CTA数据上显著优于现有方法，且通过显式分离视觉与解剖证据保持可解释性。
原文摘要 · Abstract (English)
Robust segmentation of intraluminal thrombus is critical for risk assessment in Abdominal Aortic Aneurysm, yet it remains challenging due to heterogeneous thrombus features and low contrast with surrounding non-enhanced tissues. Domain shifts induced by different Computed Tomography Angiography (CTA) protocols further inhibit multi-center generalization of deep learning models. To address these challenges, we propose a patient-specific framework that integrates discriminative learning with anatomically informed priors. Our approach introduces two key components: (1) a patient-specific intensity normalization based on a Gaussian Mixture Model of local anatomy, and (2) an Uncertainty-Gated Anatomical Attention module that incorporates spatial priors while adaptively modulating their influence according to voxel-wise confidence. This design allows for anatomical guidance in ambiguous regions while suppressing unreliable priors. The proposed method achieves state-of-the-art performance on in-distribution test data and substantially outperforms existing alternatives in generalization to external multi-center CTA data, while remaining interpretable through an explicit separation of visual and anatomical evidence.
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