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Type: Conference paper
Title: Cardiac left ventricle segmentation using convolutional neural network regression
Author: Tan, L.
Liew, Y.
Lim, E.
McLaughlin, R.
Citation: Proceedings of the Institution of Civil Engineers: Civil Engineering, 2017, pp.490-493
Publisher: IEEE
Issue Date: 2017
Series/Report no.: IEEE EMBS Conference on Biomedical Engineering and Sciences
ISBN: 9781467377911
ISSN: 0965-089X
Conference Name: IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES 2016) (4 Dec 2016 - 8 Dec 2016 : Kuala Lumpur, MALAYSIA)
Statement of
Li Kuo Tan, Yih Miin Liew, Einly Lim, Robert A. McLaughlin
Abstract: Cardiac MRI is important for the diagnosis and assessment of various cardiovascular diseases. Automated segmentation of the left ventricular (LV) endocardium at enddiastole (ED) and end-systole (ES) enables automated quantification of various clinical parameters including ejection fraction. Neural networks have been used for general image segmentation, usually via per-pixel categorization e.g. “foreground” and “background”. In this paper we propose that the generally circular LV endocardium can be parameterized and the endocardial contour determined via neural network regression. We designed two convolutional neural networks (CNN), one for localization of the LV, and the other for determining the endocardial radius. We trained the networks against 100 datasets from the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2011 challenge, and tested the networks against 45 datasets from the MICCAI 2009 challenge. The networks achieved 0.88 average Dice metric, 2.30 mm average perpendicular distance, and 97.9% good contours, the latter being the highest published result to date. These results demonstrate that CNN regression is a viable and highly promising method for automated LV endocardial segmentation at ED and ES phases, and is capable of generalizing learning between highly distinct training and testing data sets.
Keywords: Cardiac MRI; left ventricle; segmentation; neural network regression
Rights: ©2016 IEEE
DOI: 10.1109/IECBES.2016.7843499
Grant ID: ARC
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