Anatomy-Informed Deep Learning for Abdominal Aortic Aneurysm Segmentation

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.

Computed tomography image segmentation of complex abdominal aortic aneurysms (AAA) often fails because the models assign internal focus to irrelevant structures or do not focus on thin, low-contrast targets. Where the model looks is the primary training signal, and thus we propose an Explainable AI (XAI) guided encoder shaping framework. Our method computes a dense, attribution-based encoder focus map ("XAI field") from the final encoder block and uses it in two complementary ways: (i) we align the predicted probability mass to the XAI field to promote agreement between focus and output; and (ii) we route the field into a lightweight refinement pathway and a confidence prior that modulates logits at inference, suppressing distractors while preserving subtle structures. The objective terms serve only as control signals; the contribution is the integration of attribution guidance into representation and decoding. We evaluate clinically validated challenging cases curated for failure-prone scenarios. Compared to a base SAM setup, our implementation yields substantial improvements. The observed gains suggest that explicitly optimizing encoder focus via XAI guidance is a practical and effective principle for reliable segmentation in complex scenarios.

The TopAneu 2026 challenge requires intracranial aneurysms to be segmented at the voxel level and assigned to one of more than fifty vessel-specific classes, on CT and MR angiography alike. We address it with a six-stage anatomy-aware pipeline rather than a single segmentation network. A logistic-regression classifier routes each scan to a modality-specific branch; networks segment the vasculature, first as a binary mask and then over the full 36-class arterial taxonomy; a candidate localiser turns raw angiography and vessel priors into suspicious regions; a candidate-centred network segments the lesion in each crop; connected components become instances; and a per-instance classifier assigns each its anatomical location. Two principles organise the design. Whenever an upstream model output becomes a downstream training input it is generated out-of-fold, so no stage trains on unrealistically clean predictions of itself. And the anatomical decision is made once per lesion rather than once per voxel, which fits the data: of 406 connected aneurysm components in the full release, 405 carry exactly one location label. Task 1 is read off the Task 2 mask, making the two consistent by construction. We report stage-wise performance and a five-part ablation isolating what vessel priors and class-aware sampling contribute at each stage.