  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).
The 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.
