Intracranial aneurysm segmentation on digital subtraction angiography: a retrospective and multi-center study

Accurate segmentation of intracranial aneurysms (IAs) in digital subtraction angiography (DSA) is critical for endovascular embolization and risk assessment of ruptured IAs. However, this task remains challenging due to problems like vascular overlap, small target size and similarity to ring blood vessels. To develop a novel deep learning model to improve segmentation performance of IAs on DSA datassets, especially addressing challenges of small IAs.

We propose a novel deep learning model, the Shape-aware dual-stream attention network (SDAN). This network integrates two novel modules: (1) Edge-aware Local Attention Module (ELAM), which differentiates aneurysms from adjacent vasculature by capturing morphological features, (2) Global Shape-aware Fusion Block (GSFB) that enhances pattern recognition through contextual aggregation between domains. The model was trained and tested on 62,187 retrospective DSA images from three institutions, with external validation on 26,415 images. Performance was evaluated using DSC, HD95, and sensitivity.

The proposed SDAN outperforms the other models when tested on multiple centers separately with an average Dice score of 0.951 on the internal test set and 0.944 on the external test set. We also evaluated the different sizes of aneurysms individually and the results show that SDAN outperforms the other models on all sizes of aneurysms. This study demonstrates the effectiveness of SDAN for intracranial aneurysm segmentation.

Our proposed SDAN significantly improves the accurate segmentation of intracranial aneurysms in DSA images beyond existing medical image segmentation models. The model solves the problems of small intracranial aneurysms that are not easily segmented accurately, over-segmentation caused by the similarity of intracranial aneurysms and ring vessels, and under-segmentation caused by the overlap of neighboring vessels.