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

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Abstract

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.

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Funding

This work was supported in part by the National Natural Science Foundation of China under grants U1908210, 11302195, and 61976191; in part by the Natural Science Foundation of Zhejiang Province under grants LQ20H160052, LY19F030015, and LY20H180006; in part by the Zhejiang Provincial Research Project on the Application of Public Welfare Technologies under Grant LGF22F020023.

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Chenglu Zhu, Xiaoyan Wang, Ming Xia & Zhanpeng Shao

  2. School of Computer Science and Technology, Tianjin University of Technology, Tianjin, 300384, China

    Shengyong Chen & Cong Bai

  3. Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK

    Zhongzhao Teng

  4. The Second Affiliated Hospital, Zhejiang University, Hangzhou, 310009, China

    Xiaojie Huang & Zheng Gu

  5. School of Information Technology, Zhejiang Police Vocational Academy, Hangzhou, 310018, China

    Peiliang Sun

  6. School of Engineering, Westlake University, Hangzhou, 310024, China

    Chenglu Zhu

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  1. Chenglu Zhu
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  2. Xiaoyan Wang
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Correspondence to Xiaoyan Wang.

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Zhu, C., Wang, X., Chen, S. et al. Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning. Med Biol Eng Comput 60, 2693–2706 (2022). https://doi.org/10.1007/s11517-022-02622-z

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