分割
人工智能
计算机科学
计算机视觉
残余物
管腔(解剖学)
模式识别(心理学)
图像分割
颈总动脉
颈动脉
算法
医学
内科学
外科
作者
Chenglu Zhu,Xiaoyan Wang,Zhongzhao Teng,Shengyong Chen,Xiaojie Huang,Ming Xia,Lizhao Mao,Cong Bai
标识
DOI:10.1088/1361-6560/abd4bb
摘要
Abstract 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.
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