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Fully automatic myocardial segmentation of contrast echocardiography sequence using random forests guided by shape model

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Title: Fully automatic myocardial segmentation of contrast echocardiography sequence using random forests guided by shape model
Authors: Li, Y
Ho, CP
Toulemonde, M
Chahal, N
Senior, R
Tang, MX
Item Type: Journal Article
Abstract: Myocardial contrast echocardiography (MCE) is an imaging technique that assesses left ventricle function and myocardial perfusion for the detection of coronary artery diseases. Automatic MCE perfusion quantification is challenging and requires accurate segmentation of the myocardium from noisy and time-varying images. Random forests (RF) have been successfully applied to many medical image segmentation tasks. However, the pixel-wise RF classifier ignores contextual relationships between label outputs of individual pixels. RF which only utilizes local appearance features is also susceptible to data suffering from large intensity variations. In this paper, we demonstrate how to overcome the above limitations of classic RF by presenting a fully automatic segmentation pipeline for myocardial segmentation in full-cycle 2D MCE data. Specifically, a statistical shape model is used to provide shape prior information that guide the RF segmentation in two ways. First, a novel shape model (SM) feature is incorporated into the RF framework to generate a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to refine and constrain the final segmentation to plausible myocardial shapes. We further improve the performance by introducing a bounding box detection algorithm as a preprocessing step in the segmentation pipeline. Our approach on 2D image is further extended to 2D+t sequences which ensures temporal consistency in the final sequence segmentations. When evaluated on clinical MCE datasets, our proposed method achieves notable improvement in segmentation accuracy and outperforms other state-of-the-art methods including the classic RF and its variants, active shape model and image registration.
Issue Date: 1-May-2018
Date of Acceptance: 27-Aug-2017
URI: http://hdl.handle.net/10044/1/50535
DOI: https://dx.doi.org/10.1109/TMI.2017.2747081
ISSN: 0278-0062
Publisher: IEEE
Start Page: 1081
End Page: 1091
Journal / Book Title: IEEE Transactions on Medical Imaging
Volume: 37
Issue: 5
Copyright Statement: © 2017 The Author(s). This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/M011933/1
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
Random forest
statistical shape model
contrast echocardiography
myocardial segmentation
convolutional neural network
BOUNDARY DETECTION
LEFT-VENTRICLE
IMAGES
LOCALIZATION
08 Information And Computing Sciences
09 Engineering
Nuclear Medicine & Medical Imaging
Publication Status: Published online
Online Publication Date: 2017-09-26
Appears in Collections:Bioengineering
Computing
National Heart and Lung Institute
Faculty of Engineering