Clinically oriented deep learning framework for automated vessel wall segmentation in black-blood MRI: a multi-center...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Research

Background:

• Accurate segmentation of intracranial and carotid vessel walls is crucial for assessing cerebrovascular disease.
• Current manual segmentation methods are time-consuming and prone to inter-observer variability.
• Black-blood magnetic resonance vessel wall imaging (MR-VWI) provides detailed vessel wall information but requires robust segmentation techniques.

Purpose of the Study:

• To develop and validate a clinically applicable deep learning framework for automated segmentation of intracranial and carotid vessel walls.
• To enhance the accuracy and reproducibility of vessel wall segmentation in black-blood MR-VWI.
• To provide a practical tool for streamlining cerebrovascular risk assessment.

Main Methods:

• A retrospective multi-center study involving 193 patients and high-resolution black-blood MR-VWI data.
• Development of a deep learning framework incorporating polar coordinate mapping, feature-sharing padding, and a polar Dice loss function.
• External validation on independent multi-center datasets and the MICCAI 2021 Vessel Wall Segmentation Challenge dataset; interpretability using Grad-CAM.

Main Results:

• The deep learning model achieved high segmentation accuracy on external test sets, with Dice Similarity Coefficients (DSCs) of 0.928 (outer wall), 0.936 (lumen), and 0.844 (vessel wall).
• The model significantly outperformed four benchmark networks in boundary and area accuracy.
• It achieved the highest vessel wall DSC (0.782) on the public MICCAI dataset and demonstrated consistent focus on relevant anatomical boundaries via Grad-CAM.

Conclusions:

• The developed deep learning-based method enables accurate and reproducible vessel wall segmentation in clinical black-blood MR-VWI.
• This framework offers a practical solution for cerebrovascular risk assessment, supporting decision-making in stroke prevention and monitoring.
• Reliable segmentation facilitates objective quantification of intracranial atherosclerosis for early diagnosis and treatment planning.