An Anatomy-Aware Staged Pipeline for Vessel-Specific Intracranial Aneurysm Segmentation in CT and MR Angiography

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.