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

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

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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.

myocardial infarctioncardiac magnetic resonance with late gadolinium enhancementautomatic scar segmentation

Zheng-hong WU、Li-ping SUN、Yun-long LIU、Dian-dian DONG、Lv TONG、Dong-dong DENG、Yi HE、Hui WANG、Yi-bo SUN、Jian-zeng DONG、Ling XIA

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

Department of Cardiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

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

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

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

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National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaKey Research and Development Program of Zhejiang ProvinceDalian University of Technology

2016YFC1301002819018418167165081971569615278112020C03016DUT18RC3068

2021

10.1007/s11596-021-2360-z

当代医学科学(英文)
同济医科大学

当代医学科学(英文)

SCI
ISSN：2096-5230
年,卷(期)：2021.41(2)
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