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Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning.

Carotid atherosclerosis is one of the leading causes of cardiovascular disease with high mortality. Multi-contrast MRI can identify atherosclerotic plaque components with high sensitivity and specificity. Accurate segmentation of the diseased carotid artery from MR images is very essential to quanti...

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Published in:Medical & Biological Engineering & Computing Vol. 60; no. 9; pp. 2693 - 2707
Main Authors: Zhu, Chenglu, Wang, Xiaoyan, Chen, Shengyong, Teng, Zhongzhao, Bai, Cong, Huang, Xiaojie, Xia, Ming, Shao, Zhanpeng, Gu, Zheng, Sun, Peiliang
Format: Journal Article
Published: Springer Nature Sep2022
Online Access:View this record in EBSCOhost
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      jtl: Medical & Biological Engineering & Computing
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      dt: Sep2022
      vid: 60
      iid: 9
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s11517-022-02622-z
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        atl: Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning.
      aug:
        au:
          Zhu, Chenglu
          Wang, Xiaoyan
          Chen, Shengyong
          Teng, Zhongzhao
          Bai, Cong
          Huang, Xiaojie
          Xia, Ming
          Shao, Zhanpeng
          Gu, Zheng
          Sun, Peiliang
        affil: School of Computer Science and Technology, Zhejiang University of Technology, 310023, Hangzhou, China
      sug:
        subj:
          Atherosclerosis
          Carotid Artery Diseases
          Carotid Arteries
          Image Processing, Computer Assisted Methods
          Magnetic Resonance Imaging
      ab: Carotid atherosclerosis is one of the leading causes of cardiovascular disease with high mortality. Multi-contrast MRI can identify atherosclerotic plaque components with high sensitivity and specificity. Accurate segmentation of the diseased carotid artery from MR images is very essential to quantitatively evaluate the state of atherosclerosis. However, due to the complex morphology of atherosclerosis plaques and the lack of well-annotated data, the segmentation of lumen and wall is very challenging. Different from popular deep learning methods, in this paper, we propose an integration segmentation framework by introducing a lightweight prediction model and improved optimal surface graph cuts (OSG), which adopts a simplified flow line sampling and post-reconstructing method to reduce the cost of graph construction. Moreover, a flexibly adaptive smoothing penalty is presented for maintaining the shape of diseased carotid surface. For the experiments, we have collected an MR image dataset from patients with carotid atherosclerosis and evaluated our method by cross-validation. It can reach 89.68%/80.29% of dice coefficients and 0.2480 mm/0.3396 mm of average surface distances on the lumen/wall segmentation, respectively. The experimental results show that our method can generate precise and reliable segmentation of both lumen and wall of diseased carotid artery with a quite small training cost.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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