Abstract
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.
Original language | English |
---|---|
Title of host publication | Statistical Atlases and Computational Models of the Heart |
Subtitle of host publication | ACDC and MMWHS Challenges - 8th International Workshop, STACOM 2017, Revised Selected Papers |
Editors | Olivier Bernard, Pierre-Marc Jodoin, Xiahai Zhuang, Guang Yang, Alistair Young, Maxime Sermesant, Alain Lalande, Mihaela Pop |
Place of Publication | Cham |
Publisher | Springer |
Pages | 101-110 |
Number of pages | 10 |
ISBN (Electronic) | 978-3-319-75541-0 |
ISBN (Print) | 978-3-319-75540-3 |
DOIs | |
Publication status | Published - 1 Jan 2018 |
Externally published | Yes |
Event | 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017 - Quebec City, Canada Duration: 10 Sept 2017 → 14 Sept 2017 Conference number: 8 |
Publication series
Name | Lecture Notes in Computer Science |
---|---|
Publisher | Springer |
Volume | 10663 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Workshop
Workshop | 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017 |
---|---|
Abbreviated title | STACOM |
Country/Territory | Canada |
City | Quebec City |
Period | 10/09/17 → 14/09/17 |
Keywords
- Automatic diagnosis
- Cardiac MR
- Convolutional neural networks
- Deep learning
- Random forest