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Proceedings Paper

3D scar segmentation from LGE-MRI using a continuous max-flow method
Author(s): Fatma Usta; Wail Gueaieb; James A. White; Eranga Ukwatta
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Paper Abstract

Myocardial scar, a non-viable tissue which forms in the myocardium due to insufficient blood supply to the heart muscle, is one of the leading causes of life-threatening heart disorders, including arrhythmias. Accurate reconstruction of myocardial scar geometry is important for diagnosis and clinicial prognosis of the patients with ischemic cardiomyopathy. The 3D late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is increasingly being investigated for assessing myocardial tissue viability. For applications, such as computational modeling of cardiac electrophysiology aimed at stratifying patient risk for post-infarction arrhythmias, segmentation and reconstruction of the intact geometry of scar is required. However, manual analysis and segmentation of myocardial scar from 3D LGE-MRI is a tedious task. Therefore, semi-automated and fully-automated segmentation algorithms are highly desirable in a clinical setting. In this study, we developed an approach to segment the myocardial scar from 3D LGE-MR images using a continuous max-flow (CMF) method. The data term comprised of a distribution matching term for scar and normal myocardium and a boundary smoothness term for the scar boundaries. The region-of-interest for the scar segmentation is constrained, using manually segmented myocardium. We evaluated our CMF method for accuracy by comparing it to manual scar delineations using 3D LGE-MR images of 34 patients. We compare the results of the CMF technique to ones by conventional full-width-at-half-maximum (FWHM) and signal-threshold-to-reference-mean (STRM) methods. The CMF method yielded a Dice similarity coefficient (DSC) of 72±18% and an absolute volume error (|V E|) of 15.42±14.1 cm3. Overall, the CMF method outperformed the state-of-the-art methods for all reported metrics in 3D scar segmentation except for the recall value which STRM 2-SD performed better than CMF on average.

Paper Details

Date Published: 12 March 2018
PDF: 9 pages
Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105780U (12 March 2018); doi: 10.1117/12.2294406
Show Author Affiliations
Fatma Usta, Univ. of Ottawa (Canada)
Carleton Univ. (Canada)
Wail Gueaieb, Univ. of Ottawa (Canada)
James A. White, Univ. of Calgary (Canada)
Eranga Ukwatta, Carleton Univ. (Canada)

Published in SPIE Proceedings Vol. 10578:
Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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