Automatic 3D TOF-MRA Intracranial Aneurysm Segmentation using SAM-Guided Multi-View Decoding Network.
Authors
Affiliations (6)
Affiliations (6)
- Department of Medical Imaging, Jinhua Municipal Central Hospital, Jinhua 321000, China.
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China.
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China.
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China. Electronic address: [email protected].
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China. Electronic address: [email protected].
Abstract
Deep learning-based aneurysm segmentation using 3D time-of-flight magnetic resonance angiography (TOF-MRA) offers promising non-invasive screening, but its accuracy and generalizability remain limited. To develop a deep learning model for intracranial aneurysm detection and segmentation on MRA, and evaluate its multicenter performance against the digital subtraction angiography (DSA) gold standard and radiologists of varying experience. MRA images from patients suspected of harboring unruptured intracranial aneurysms were retrospectively collected from Jinhua Municipal Central Hospital for model development. An external test set, with DSA as the reference standard, was gathered from Sir Run Run Shaw Hospital, Zhejiang University School of Medicine. Manual segmentation of cerebral arteries and aneurysms was performed using 3D-Slicer software. Performance was evaluated via Dice Similarity Coefficient (DSC), sensitivity, and F1-score, and compared against independent clinical evaluations from radiologists with different experience levels. The internal dataset comprised 1,560 patients (1,554 aneurysms), including 421 patients (522 aneurysms) for testing. The external test set included 208 patients (224 aneurysms). On the internal test set, the model achieved a lesion-level sensitivity of 86.0% (95% CI: 82.8 - 88.7), a precision of 87.4% (95% CI: 84.2, 90.0), and a DSC of 0.88 (95% CI: 0.84, 0.90). On the independent external test set, the model demonstrated robust generalizability with a sensitivity of 83.4% (95% CI: 75.9, 88.0) and a DSC of 0.84 (95% CI: 0.79, 0.87). The model achieved an overall detection F1-score of 86.7, significantly outperforming junior radiologists (P <.001) and was comparable to mid-level radiologists (P >.05), with an average processing time of approximately 72seconds per exam. The proposed deep learning model demonstrated robust multicenter performance for intracranial aneurysm detection and segmentation. Achieving diagnostic accuracy comparable to mid-level radiologists, it holds potential as an efficient assistive tool for MRA interpretation in clinical workflows.