Automatic Segmentation of LV and RV in Cardiac MRI

  • Yeonggul Jang
  • Yoonmi Hong
  • Seongmin Ha
  • Sekeun Kim
  • Hyuk-Jae Chang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)


Automatic and accurate segmentation of Left Ventricle (LV) and Right Ventricle (RV) in cine-MRI is required to analyze cardiac function and viability. We present a fully convolutional neural network to efficiently segment LV and RV as well as myocardium. The network is trained end-to-end from scratch. Average dice scores from five-fold cross-validation on the ACDC training dataset were 0.94, 0.89, and 0.88 for LV, RV, and myocardium. Experimental results show the robustness of the proposed architecture.


Cardiac segmentation Convolutional neural network Cardiac MRI Automated Cardiac Diagnosis Challenge 



This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2017-0-00255, Autonomous digital companion framework and application).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yeonggul Jang
    • 1
  • Yoonmi Hong
    • 2
  • Seongmin Ha
    • 2
  • Sekeun Kim
    • 2
  • Hyuk-Jae Chang
    • 2
    • 3
  1. 1.Brain Korea 21 PLUS Project for Medical ScienceYonsei UniversitySeoulSouth Korea
  2. 2.Integrative Cardiovascular Imaging Research CenterYonsei University College of MedicineSeoulSouth Korea
  3. 3.Division of Cardiology, Severance Cardiovascular HospitalYonsei University College of MedicineSeoulSouth Korea

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