Objective To investigate the value of automatic segmentation of carotid vessel wall in multicontrast MR images using U?Net neural network. Methods Patients were retrospectively collected from 2012 to 2015 in Carotid Atherosclerosis Risk Assessment (CARE II) study. All patients who recently suffered ischemic stroke and/or transient ischemic attack underwent identical, state?of?the?art multicontrast MRI technique. A total of 17 568 carotid vessel wall MR images from 658 subjects were included in this study after inclusion criteria and exclusion criteria. All MR images were analyzed using customized analysis platform (CASCADE). Randomly, 10 592 images were assigned into training dataset, 3 488 images were assigned into validating dataset and 3 488 images were assigned into test dataset according to a ratio of 6∶2∶2. Data augmentation was performed to avoid over fitting and improve the ability of model generalization. The fine?tuned U?Net model was utilized in the segmentation of carotid vessel wall in multicontrast MR images. The U?Net model was trained in the training dataset and validated in the validating dataset. To evaluate the accuracy of carotid vessel wall segmentation, the sensitivity, specificity and Dice coefficient were used in the testing dataset. In addition, the interclass correlation and the Bland?Altman analysis of max wall thickness and wall area were obtained to demonstrate the agreement of the U?Net segmentation and the manual segmentation. Results The sensitivity, specificity and Dice coefficient of the fine?tuned U?Net model achieved 0.878,0.986 and 0.858 in the test dataset, respectively. The interclass correlation (95% confidence interval) was 0.921 (0.915-0.925) for max wall thickness and 0.929 (0.924-0.933) for wall area. In the Bland?Altman analysis, the difference of max wall thickness was (0.037±0.316) mm and the difference of wall area was (1.182±4.953) mm2. The substantial agreement was observed between U?Net segmentation method and manual segmentation method. Conclusion Automatic segmentation of carotid vessel wall in multicontrast MR images can be achieved using fine?tuned U?Net neural network, which is trained and tested in the large scale dataset labeled by professional radiologists.

Objective: To investigate the value of automatic segmentation of carotid vessel wall in multicontrast MR images using U-Net neural network. Methods: Patients were retrospectively collected from 2012 to 2015 in Carotid Atherosclerosis Risk Assessment (CARE II) study. All patients who recently suffered ischemic stroke and/or transient ischemic attack underwent identical, state-of-the-art multicontrast MRI technique. A total of 17 568 carotid vessel wall MR images from 658 subjects were included in this study after inclusion criteria and exclusion criteria. All MR images were analyzed using customized analysis platform (CASCADE). Randomly, 10 592 images were assigned into training dataset, 3 488 images were assigned into validating dataset and 3 488 images were assigned into test dataset according to a ratio of 6∶2∶2. Data augmentation was performed to avoid over fitting and improve the ability of model generalization. The fine-tuned U-Net model was utilized in the segmentation of carotid vessel wall in multicontrast MR images. The U-Net model was trained in the training dataset and validated in the validating dataset. To evaluate the accuracy of carotid vessel wall segmentation, the sensitivity, specificity and Dice coefficient were used in the testing dataset. In addition, the interclass correlation and the Bland-Altman analysis of max wall thickness and wall area were obtained to demonstrate the agreement of the U-Net segmentation and the manual segmentation. Results: The sensitivity, specificity and Dice coefficient of the fine-tuned U-Net model achieved 0.878,0.986 and 0.858 in the test dataset, respectively. The interclass correlation (95% confidence interval) was 0.921 (0.915-0.925) for max wall thickness and 0.929 (0.924-0.933) for wall area. In the Bland-Altman analysis, the difference of max wall thickness was (0.037±0.316) mm and the difference of wall area was (1.182±4.953) mm². The substantial agreement was observed between U-Net segmentation method and manual segmentation method. Conclusion Automatic segmentation of carotid vessel wall in multicontrast MR images can be achieved using fine-tuned U-Net neural network, which is trained and tested in the large scale dataset labeled by professional radiologists.