Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images

  • Jelmer M. Wolterink
  • Tim Leiner
  • Max A. Viergever
  • Ivana Išgum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)


Segmentation of the heart in cardiac cine MR is clinically used to quantify cardiac function. We propose a fully automatic method for segmentation and disease classification using cardiac cine MR images. A convolutional neural network (CNN) was designed to simultaneously segment the left ventricle (LV), right ventricle (RV) and myocardium in end-diastole (ED) and end-systole (ES) images. Features derived from the obtained segmentations were used in a Random Forest classifier to label patients as suffering from dilated cardiomyopathy, hypertrophic cardiomyopathy, heart failure following myocardial infarction, right ventricular abnormality, or no cardiac disease.

The method was developed and evaluated using a balanced dataset containing images of 100 patients, which was provided in the MICCAI 2017 automated cardiac diagnosis challenge (ACDC). Segmentation and classification pipeline were evaluated in a four-fold stratified cross-validation. Average Dice scores between reference and automatically obtained segmentations were 0.94, 0.88 and 0.87 for the LV, RV and myocardium. The classifier assigned 91% of patients to the correct disease category. Segmentation and disease classification took 5 s per patient.

The results of our study suggest that image-based diagnosis using cine MR cardiac scans can be performed automatically with high accuracy.


Deep learning Random Forest Convolutional neural networks Cardiac MR Automatic diagnosis 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jelmer M. Wolterink
    • 1
  • Tim Leiner
    • 2
  • Max A. Viergever
    • 1
  • Ivana Išgum
    • 1
  1. 1.Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
  2. 2.Department of RadiologyUniversity Medical Center UtrechtUtrechtThe Netherlands

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