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A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images

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Title: A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images
Authors: Bai, W
Shi, W
O'Regan, DP
Tong, T
Wang, H
Jamil-Copley, S
Peters, NS
Rueckert, D
Item Type: Journal Article
Abstract: The evaluation of ventricular function is important for the diagnosis of cardiovascular diseases. It typically involves measurement of the left ventricular (LV) mass and LV cavity volume. Manual delineation of the myocardial contours is time-consuming and dependent on the subjective experience of the expert observer. In this paper, a multi-atlas method is proposed for cardiac magnetic resonance (MR) image segmentation. The proposed method is novel in two aspects. First, it formulates a patch-based label fusion model in a Bayesian framework. Second, it improves image registration accuracy by utilizing label information, which leads to improvement of segmentation accuracy. The proposed method was evaluated on a cardiac MR image set of 28 subjects. The average Dice overlap metric of our segmentation is 0.92 for the LV cavity, 0.89 for the right ventricular cavity and 0.82 for the myocardium. The results show that the proposed method is able to provide accurate information for clinical diagnosis.
Issue Date: 1-Jul-2013
Date of Acceptance: 28-Mar-2013
URI: http://hdl.handle.net/10044/1/77207
DOI: 10.1109/TMI.2013.2256922
ISSN: 0278-0062
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 1302
End Page: 1315
Journal / Book Title: IEEE Transactions on Medical Imaging
Volume: 32
Issue: 7
Copyright Statement: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: British Heart Foundation
Funder's Grant Number: FS/11/22/28745
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Engineering
Image registration
image segmentation
multi-atlas segmentation
patch-based segmentation
MAGNETIC-RESONANCE IMAGES
NONRIGID REGISTRATION
HEART
HIPPOCAMPUS
PROPAGATION
COMBINATION
STRATEGIES
ALGORITHM
VENTRICLE
FRAMEWORK
Algorithms
Bayes Theorem
Heart
Heart Ventricles
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Heart
Heart Ventricles
Humans
Magnetic Resonance Imaging
Bayes Theorem
Algorithms
Image Processing, Computer-Assisted
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Engineering
Image registration
image segmentation
multi-atlas segmentation
patch-based segmentation
MAGNETIC-RESONANCE IMAGES
NONRIGID REGISTRATION
HEART
HIPPOCAMPUS
PROPAGATION
COMBINATION
STRATEGIES
ALGORITHM
VENTRICLE
FRAMEWORK
08 Information and Computing Sciences
09 Engineering
Nuclear Medicine & Medical Imaging
Publication Status: Published
Online Publication Date: 2013-04-05
Appears in Collections:Computing
Department of Brain Sciences
Faculty of Engineering