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Fully Automatic Scar Segmentation for Late Gadolinium Enhancement MRI Images in Left Ventricle with Myocardial Infarction

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Summary

Numerous methods have been published to segment the infarct tissue in the left ventricle, most of them either need manual work, post-processing, or suffer from poor reproducibility. We proposed an automatic segmentation method for segmenting the infarct tissue in left ventricle with myocardial infarction. Cardiac images of a total of 60 diseased hearts (55 human hearts and 5 porcine hearts) were used in this study. The epicardial and endocardial boundaries of the ventricles in every 2D slice of the cardiac magnetic resonance with late gadolinium enhancement images were manually segmented. The subsequent pipeline of infarct tissue segmentation is fully automatic. The segmentation results with the automatic algorithm proposed in this paper were compared to the consensus ground truth. The median of Dice overlap between our automatic method and the consensus ground truth is 0.79. We also compared the automatic method with the consensus ground truth using different image sources from different centers with different scan parameters and different scan machines. The results showed that the Dice overlap with the public dataset was 0.83, and the overall Dice overlap was 0.79. The results show that our method is robust with respect to different MRI image sources, which were scanned by different centers with different image collection parameters. The segmentation accuracy we obtained is comparable to or better than that of the conventional semi-automatic methods. Our segmentation method may be useful for processing large amount of dataset in clinic.

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Author notes

  1. These authors contributed equally to this study.

Authors and Affiliations

  1. College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, China

    Zheng-hong Wu & Ling Xia

  2. Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China

    Li-ping Sun, Yi-bo Sun & Jian-zeng Dong

  3. School of Biomedical Engineering, Dalian University of Technology, Dalian, 116024, China

    Yun-long Liu, Dian-dian Dong, Lv Tong & Dong-dong Deng

  4. Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University and National Clinical Research Center for Cardiovascular Diseases, Beijing, 100029, China

    Yi He, Hui Wang & Jian-zeng Dong

  5. Department of Cardiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China

    Yi He

Authors
  1. Zheng-hong Wu
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  2. Li-ping Sun
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  3. Yun-long Liu
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  4. Dian-dian Dong
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  5. Lv Tong
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  6. Dong-dong Deng
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  7. Yi He
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  8. Hui Wang
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  9. Yi-bo Sun
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  10. Jian-zeng Dong
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  11. Ling Xia
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Corresponding authors

Correspondence to Dong-dong Deng or Ling Xia.

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Conflict of Interest Statement

The authors declare that there is no conflict of interest related to the contents of this article.

This work was supported by the National Key Research and Development Program of China (No. 2016YFC1301002 to Jianzeng Dong), the National Natural Science Foun dation of China (No. 81901841 to Dongdong Deng, No. 81671650 and No. 81971569 to Yi He, No. 61527811 to Ling Xia), and the Key Research and Development Program of Zhejiang Province (No. 2020C03016 to Ling Xia). Dongdong Deng also acknowledges support from Dalian University of Technology (No. DUT18RC(3)068).

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Wu, Zh., Sun, Lp., Liu, Yl. et al. Fully Automatic Scar Segmentation for Late Gadolinium Enhancement MRI Images in Left Ventricle with Myocardial Infarction. CURR MED SCI 41, 398–404 (2021). https://doi.org/10.1007/s11596-021-2360-z

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  • DOI: https://doi.org/10.1007/s11596-021-2360-z

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