<ul data-eligibleForWebStory="false"><li>The analysis of carotid arteries, especially plaques, in multi-sequence MRI data is crucial for assessing the risk of atherosclerosis and ischemic stroke.</li><li>A semi-supervised deep learning-based approach is proposed to integrate multi-sequence MRI data for accurate segmentation of carotid artery vessel wall and plaque.</li><li>The approach includes a coarse localization model followed by a fine segmentation model, along with fusion strategies and a multi-level multi-sequence U-Net architecture.</li><li>The method addresses challenges of limited labeled data and complex carotid artery MRI through consistency enforcement under various input transformations, showcasing promising results.</li></ul>

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation

Discover more