TY - GEN
T1 - Automatic segmentation and disease classification using cardiac cine MR images
AU - Wolterink, Jelmer M.
AU - Leiner, Tim
AU - Viergever, Max A.
AU - Išgum, Ivana
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85044445499&origin=inward
U2 - https://doi.org/10.1007/978-3-319-75541-0_11
DO - https://doi.org/10.1007/978-3-319-75541-0_11
M3 - Conference contribution
SN - 9783319755403
VL - 10663 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 101
EP - 110
BT - Statistical Atlases and Computational Models of the Heart: ACDC and MMWHS Challenges - 8th International Workshop, STACOM 2017, Revised Selected Papers
A2 - Bernard, Olivier
A2 - Jodoin, Pierre-Marc
A2 - Zhuang, Xiahai
A2 - Yang, Guang
A2 - Young, Alistair
A2 - Sermesant, Maxime
A2 - Lalande, Alain
A2 - Pop, Mihaela
PB - Springer Verlag
T2 - 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017
Y2 - 10 September 2017 through 14 September 2017
ER -