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Proceedings Paper

Myocardial scar segmentation from magnetic resonance images using convolutional neural network
Author(s): Fatemeh Zabihollahy; James A. White; Eranga Ukwatta
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Paper 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 intensitybased 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 algorithmgenerated 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.

Paper Details

Date Published: 27 February 2018
PDF: 8 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105752Z (27 February 2018); doi: 10.1117/12.2293518
Show Author Affiliations
Fatemeh Zabihollahy, Carleton Univ. (Canada)
James A. White, Univ. of Calgary (Canada)
Eranga Ukwatta, Carleton Univ. (Canada)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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