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Fully Automatic Scar Segmentation for late gadolinium enhancement MRI Images in Left Ventricle with Myocardial Infarction

引用

Current Medical Science 2021年第2期41卷 398-404页

作者： 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 College of Biomedical Engineering&Instrument Science Zhejiang UniversiryHangzhou 310027China Department of Cardiology the First Affiliated Hospital of Zhengzhou UniversiryZhengzhou 450052China School of Biomedical Engineering Dalian University of TechnologyDalian 116024China Department of Cardiology Beijing Anzhen HospitalCapital Medical University and National Clinical Research Center for Cardiovascular DiseasesBeijing 100029China Department of Cardiology Beijing Friendship HospitalCapital Medical UniversityBeijing 100050China

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

关键词： myocardial infarction cardiac magnetic resonance with late gadolinium enhancement automatic scar segmentation

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