Document Type

Article

Publication Date

5-23-2025

Department

Department of Biomedical Engineering; Joint Center of Biocomputing and Digital Health; Health Research Institute; Institute of Computing and Cybersystems

Abstract

Objective: The rupture of intracranial aneurysms leads to subarachnoid hemorrhage. Detecting intracranial aneurysms before rupture and stratifying their risk is critical in guiding preventive measures. Point-based aneurysm segmentation provides a plausible pathway for automatic aneurysm detection. However, challenges in existing segmentation methods motivate the proposed work. Methods: We propose a dual-branch network model (JGTANet) for accurately detecting aneurysms. JGTA-Net employs a hierarchical geometric feature learning framework to extract local contextual geometric information from the point cloud representing intracranial vessels. Building on this, we integrated a topological analysis module that leverages persistent homology to capture complex structural details of 3D objects, filtering out short-lived noise to enhance the overall topological invariance of the aneurysms. Moreover, we refined the segmentation output by quantitatively computing multi-scale topological features and introducing a topological loss function to preserve the correct topological relationships better. Finally, we designed a feature fusion module that integrates information extracted from different modalities and receptive fields, enabling effective multi-source information fusion. Results: Experiments conducted on the IntrA dataset demonstrated the superiority of the proposed network model, yielding state-of-the-art segmentation results (e.g., Dice and IOU are approximately 0.95 and 0.90, respectively). Our IntrA results were confirmed by testing on two independent datasets: One with comparable lengths to the IntrA dataset and the other with longer and more complex vessels. Conclusions: The proposed JGTA-Net model outperformed other recently published methods (> 10% in DSC and IOU), showing our model's strong generalization capabilities. Significance: The proposed work can be integrated into a large deep-learning-based system for assessing brain aneurysms in the clinical workflow.

Publisher's Statement

© 2025. Publisher’s version of record: https://doi.org/10.1109/TBME.2025.3572837

Publication Title

IEEE Transactions on Biomedical Engineering

Version

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