Trust the Prior (or Not): Uncertainty-Aware Abdominal Aortic Aneurysm Segmentation
For clinicians assessing AAA risk, this method improves segmentation robustness and generalization across CTA protocols, addressing a key bottleneck in multi-center deployment.
The paper tackles robust segmentation of intraluminal thrombus in Abdominal Aortic Aneurysm under domain shifts from different CTA protocols, achieving state-of-the-art performance on in-distribution data and substantially outperforming existing alternatives on external multi-center data.
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.