Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images.


Zhu C(1), Wang X(1), Teng Z(2), Chen S(3), Huang X(4), Xia M(1), Mao L(1), Bai C(1).
Author information:
(1)College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023 People's Republic of China.
(2)University Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom.
(3)Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, People's Republic of China.
(4)The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, People's Republic of China.


Accurate and automatic carotid artery segmentation for magnetic resonance (MR) images is eagerly expected, which can greatly assist a comprehensive study of atherosclerosis and accelerate the translation. Although many efforts have been made, identification of the inner lumen and outer wall in diseased vessels is still a challenging task due to complex vascular deformation, blurred wall boundary, and confusing componential expression. In this paper, we introduce a novel fully automatic 3D framework for simultaneously segmenting the carotid artery from high-resolution multi-contrast MR sequences based on deep learning. First, an optimal channel fitting structure is designed for identity mapping, and a novel 3D residual U-net is used as a basic network. Second, high-resolution MR images are trained using both patch-level and global-level strategies, and the two pre-segmentation results are optimized based on structural characteristics. Third, the optimized pre-segmentation results are cascaded with the patch-cropped MR volume data and trained to segment the carotid lumen and wall. Extensive experiments demonstrate the proposed method outperforms the state-of-the-art 3D Unet-based segmentation models.