Trust the Prior (or Not): Uncertainty-Aware Abdominal Aortic Aneurysm Segmentation

• URL: http://arxiv.org/abs/2607.00201v1
• Date: Tue, 30 Jun 2026 21:30:49 GMT
• Title: Trust the Prior (or Not): Uncertainty-Aware Abdominal Aortic Aneurysm Segmentation
• Abstract summary: We propose a patient-specific framework that integrates discriminative learning with informed priors.<n>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.
• Score: 6.7365887585568585
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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