Deep learning-based automatic segmentation of arterial vessel walls and plaques in MR vessel wall images for quantitative assessment.

Abstract

Objectives: To develop and validate a deep-learning-based automatic method for vessel walls and atherosclerotic plaques segmentation for quantitative evaluation in MR vessel wall images.

Materials and methods: A total of 193 patients (107 patients for training and validation, 39 patients for internal test, 47 patients for external test) with atherosclerotic plaque from five centers underwent T1-weighted MRI scans and were included in the dataset. The first step of the proposed method was constructing a purely learning-based convolutional neural network (CNN) named Vessel-SegNet to segment the lumen and the vessel wall. The second step is using the vessel wall priors (including manual prior and Tversky-loss-based automatic prior) to improve the plaque segmentation, which utilizes the morphological similarity between the vessel wall and the plaque. The Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), etc., were used to evaluate the similarity, agreement, and correlations.

Results: Most of the DSCs for lumen and vessel wall segmentation were above 90%. The introduction of vessel wall priors can increase the DSC for plaque segmentation by over 10%, reaching 88.45%. Compared to dice-loss-based vessel wall priors, the Tversky-loss-based priors can further improve DSC by nearly 3%, reaching 82.84%. Most of the ICC values between the Vessel-SegNet and manual methods in the 6 quantitative measurements are greater than 85% (p-value < 0.001).

Conclusion: The proposed CNN-based segmentation model can quickly and accurately segment vessel walls and plaques for quantitative evaluation. Due to the lack of testing with other equipment, populations, and anatomical studies, the reliability of the research results still requires further exploration.

Key points: Question How can the accuracy and efficiency of vessel component segmentation for quantification, including the lumen, vessel wall, and plaque, be improved? Findings Improved CNN models, manual/automatic vessel wall priors, and Tversky loss can improve the performance of semi-automatic/automatic vessel components segmentation for quantification. Clinical relevance Manual segmentation of vessel components is a time-consuming yet important process. Rapid and accurate segmentation of the lumen, vessel walls, and plaques for quantification assessment helps patients obtain more accurate, efficient, and timely stroke risk assessments and clinical recommendations.