Performance Analysis of Deep Learning Models for Segmentation of Carotid Artery Vessel Wall in 3D-MERGE Images.
Authors
Affiliations (1)
Affiliations (1)
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Abstract
Carotid vessel wall segmentation and determination of the lumen area are crucial for the diagnosis of atherosclerosis. U-Net-based deep learning models have been investigated for carotid vessel wall segmentation in magnetic resonance imaging. However, the use of these deep learning models for 3D Motion-Sensitized Driven Equilibrium-prepared Rapid Gradient Echo (3D-MERGE) imaging is less explored. In addition, the effect of preprocessing techniques on the performance of deep learning models using 3D-MERGE images need to be investigated. This paper explores deep learning-based image segmentation models for carotid artery vessel wall segmentation from 3D-MERGE images. A detailed comparative analysis of U-Net, Attention U-Net, and Residual U-Net models with different preprocessing techniques is performed on a public dataset. The efficiency of the models is analyzed using various evaluation metrics including Dice score, sensitivity, and specificity. The U-Net model achieved a Dice score of 70.85%, while the Attention U-Net gave 67.04%, showing a significant improvement ( <i>p</i> <0.05). Cross-validation analysis showed improved performance of the Attention U-Net model. Preprocessing reduced the number of erroneous detections by approximately 52% in both U-Net and Attention U-Net models. Among the three models, the Residual U-Net model underperformed in the segmentation of carotid vessel walls. Our findings show that the U-Net and Attention U-Net models have great potential for detecting carotid vessels in 3D-MERGE images. Image preprocessing has a notable impact on the training of U-Net-based models.