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A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging.

Objectives. The purpose of this study was to segment the left ventricle (LV) blood pool, LV myocardium, and right ventricle (RV) blood pool of end-diastole and end-systole frames in free-breathing cardiac magnetic resonance (CMR) imaging. Automatic and accurate segmentation of cardiac structures cou...

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Published in:BioMed Research International pp. 1 - 13
Main Authors: Yang, Fan, Zhang, Yan, Lei, Pinggui, Wang, Lihui, Miao, Yuehong, Xie, Hong, Zeng, Zhu
Format: diagnostic images equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 7/30/2019
Online Access:View this record in EBSCOhost
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        23146133
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      jtl: BioMed Research International
      issn: 23146133
      maglogo: N
    pubinfo:
      dt: 7/30/2019
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        137787242
        137787242
        137787242
        10.1155/2019/5636423
        137787242
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      tig:
        atl: A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging.
      aug:
        au:
          Yang, Fan
          Zhang, Yan
          Lei, Pinggui
          Wang, Lihui
          Miao, Yuehong
          Xie, Hong
          Zeng, Zhu
        affil: Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang 550025, China
      sug:
        subj:
          Cardiovascular System Physiology
          Diagnostic Imaging Methods
          Magnetic Resonance Imaging Methods
          Heart Ventricle Anatomy and Histology
          Image Interpretation, Computer Assisted Methods
          Blood Circulation
          Deep Learning
          Human
          Respiration
          Machine Learning
          Image Processing, Computer Assisted Methods
          Random Sample
      ab: Objectives. The purpose of this study was to segment the left ventricle (LV) blood pool, LV myocardium, and right ventricle (RV) blood pool of end-diastole and end-systole frames in free-breathing cardiac magnetic resonance (CMR) imaging. Automatic and accurate segmentation of cardiac structures could reduce the postprocessing time of cardiac function analysis. Method. We proposed a novel deep learning network using a residual block for the segmentation of the heart and a random data augmentation strategy to reduce the training time and the problem of overfitting. Automated cardiac diagnosis challenge (ACDC) data were used for training, and the free-breathing CMR data were used for validation and testing. Results. The average Dice was 0.919 (LV), 0.806 (myocardium), and 0.818 (RV). The average IoU was 0.860 (LV), 0.699 (myocardium), and 0.761 (RV). Conclusions. The proposed method may aid in the segmentation of cardiac images and improves the postprocessing efficiency of cardiac function analysis.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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