Fully automatic myocardial segmentation of contrast echocardiography sequence using random forests guided by shape model
File(s)
Published version
Author(s)
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.
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.
Date Issued
2018-05-01
Date Acceptance
2017-08-27
Citation
IEEE Transactions on Medical Imaging, 2018, 37 (5), pp.1081-1091
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/.
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/M011933/1
Subjects
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
Date Publish Online
2017-09-26