Open AccessA Joint Geometric Topological Analysis Network (JGTA-Net) for Detecting and Segmenting Intracranial Aneurysms
Author(s) -
Xinyue Zhang,
Zonghan Lyu,
Yang Wang,
Bo Peng,
Jingfeng Jiang
Publication year - 2025
Publication title -
ieee transactions on biomedical engineering
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 1.148
H-Index - 200
eISSN - 1558-2531
pISSN - 0018-9294
DOI - 10.1109/tbme.2025.3572837
Subject(s) - bioengineering , computing and processing , components, circuits, devices and systems , communication, networking and broadcast technologies
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.