Paper Info
Title
Myocardial scar segmentation from magnetic resonance images using convolutional neural network.
Abstract
Accurate segmentation of the myocardial fibrosis or scar may provide important advancements for the prediction and management of malignant ventricular arrhythmias in patients with cardiovascular disease. In this paper, we propose a semi-automated method for segmentation of myocardial scar from late gadolinium enhancement magnetic resonance image (LGE-MRI) using a convolutional neural network (CNN). In contrast to image intensity-based methods, CNN based algorithms have the potential to improve the accuracy of scar segmentation through the creation of high-level features from a combination of convolutional, detection and pooling layers. Our developed algorithm was trained using 2,336,703 image patches extracted from 420 slices of five 3D LGE-MR datasets, then validated on 2,204,178 patches from a testing dataset of seven 3D LGE-MR images including 624 slices, all obtained from patients with chronic myocardial infarction. For evaluation of the algorithm, we compared the algorithm-generated segmentations to manual delineations by experts. Our CNN-based method reported an average Dice similarity coefficient (DSC), precision, and recall of 94.50 +/- 3.62%, 96.08 +/- 3.10%, and 93.96 +/- 3.75% as the accuracy of segmentation, respectively. As compared to several intensity threshold-based methods for scar segmentation, the results of our developed method have a greater agreement with manual expert segmentation.
Year
DOI
Venue
2018
10.1117/12.2293518
Proceedings of SPIE
Field
DocType
Volume
Pattern recognition,Convolutional neural network,Segmentation,Computer science,Artificial intelligence,Deep learning,Magnetic resonance imaging
Conference
10575
ISSN
Citations 
PageRank 
0277-786X
1
0.36
References 
Authors
0
3
Name
Order
Citations
PageRank
Fatemeh Zabihollahy132.11
James A. White2527.70
Eranga Ukwatta315418.10