Anatomically-guided Masked Autoencoder with Domain-Adaptive Prompting (AMAP) for multimodal cerebral aneurysm detection and segmentation

Abstract

Intracranial cerebral aneurysms are life-threatening vascular abnormalities whose rupture may result in subarachnoid hemorrhage, stroke, or death. Detecting and delineating aneurysms, particularly those under 5 mm, is essential for risk assessment and treatment planning but remains difficult for current AI approaches. Existing methods often fail to identify small aneurysms, mis-segment vascular bifurcations, and show reduced performance across imaging centers and modalities. We introduce AMAP (Anatomically-guided Masked Autoencoder with domain-adaptive Prompting), a framework for reliable cerebral aneurysm analysis. AMAP incorporates three key components: (1) anatomy-guided MAE pretraining, which directs self-supervised reconstruction toward cerebrovascular structures and captures subtle aneurysm morphology; (2) domain-adaptive prompting, which combines global vascular priors with case-specific prompts to enhance robustness across domains; and (3) boundary-aware contrastive learning with GS-EMA, which aligns vessel boundaries and mitigates false positives at bifurcations. Experiments on three public datasets (ADAM, IntrA, CQ500) and additional unseen domains demonstrate that AMAP surpasses CNN-, Transformer-, and foundation-based baselines, as well as domain generalization methods. It achieves 3−5% higher Dice scores, reduces false positives per case by about 20%, and improves calibration. Qualitative results further show accurate boundary preservation and consistent detection of small aneurysms overlooked by other methods. These findings suggest that AMAP is a step toward trustworthy and clinically applicable AI for aneurysm screening.