Clinically oriented deep learning framework for automated vessel wall segmentation in black-blood MRI: a multi-center study.
Authors
Affiliations (11)
Affiliations (11)
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, State Key Laboratory of Biomedical Imaging Science and System, Shenzhen, China.
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
- Hangzhou Institute of Extremely-Weak Magnetic Field Major National Science and Technology Infrastructure, Hangzhou, China.
- Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, China.
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, China.
- First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China.
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. [email protected].
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China. [email protected].
- State Key Laboratory of Biomedical Imaging Science and System, Shenzhen, China. [email protected].
Abstract
To develop and validate a clinically applicable deep learning framework for automated segmentation of intracranial and carotid vessel walls in black-blood magnetic resonance vessel wall imaging (MR-VWI). In this retrospective multi-center study, 193 patients (mean age: 60.2 ± 4.3 years) from five hospitals underwent high-resolution black-blood MR-VWI. A deep learning segmentation framework was developed incorporating three key innovations: (1) polar coordinate mapping, (2) a feature-sharing padding strategy, and (3) a polar Dice loss function. Manual expert annotations served as the reference standard for training and evaluation. Model performance was assessed using Dice similarity coefficients (DSC), Hausdorff distances (HD), and area differences (AD) for both lumen and vessel wall regions. External validation was performed on an independent multi-center test set from four external institutions, and the publicly available MICCAI 2021 Vessel Wall Segmentation Challenge dataset. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for interpretability. On the external test set, the model achieved DSCs of 0.928 (outer wall area), 0.936 (lumen area), and 0.844 (vessel wall region). It significantly outperformed four benchmark networks in boundary and area accuracy (all p < 0.05). On the public MICCAI dataset, it achieved the highest vessel wall DSC (0.782) and the lowest lumen and wall area errors. Grad-CAM confirmed that the model consistently focused on anatomically relevant vessel wall boundaries. This deep learning-based method enables accurate and reproducible vessel wall segmentation in clinical black-blood MR-VWI, offering a practical solution to streamline cerebrovascular risk assessment and support decision-making in stroke prevention and monitoring. Question Can a clinically oriented deep learning framework provide accurate, generalizable vessel wall segmentation across diverse vascular territories in black-blood MR imaging? Findings The proposed model achieved superior segmentation performance compared to four benchmark networks, with consistent accuracy on multi-center and public test datasets. Clinical relevance Reliable vessel wall segmentation may support more objective quantification of intracranial atherosclerosis, enabling early diagnosis, treatment planning, and longitudinal monitoring of high-risk stroke patients.