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A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images
File | Description | Size | Format | |
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draft_final.pdf | Accepted version | 701.04 kB | Adobe PDF | View/Open |
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 |