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Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography

Norlén, Alexander; Alvén, Jennifer; Molnar, David; Enqvist, Olof; Norrlund, Rauni Rossi; Brandberg, John; Bergström, Göran and Kahl, Fredrik LU (2016) In Journal of Medical Imaging 3(3).
Abstract

Recent findings indicate a strong correlation between the risk of future heart disease and the volume of adipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delineation of the pericardium is extremely time-consuming and that existing methods for automatic delineation lack accuracy. An efficient and fully automatic approach to pericardium segmentation and epicardial fat volume (EFV) estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a random forest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated computer tomography angiography volumes shows a significant improvement on... (More)

Recent findings indicate a strong correlation between the risk of future heart disease and the volume of adipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delineation of the pericardium is extremely time-consuming and that existing methods for automatic delineation lack accuracy. An efficient and fully automatic approach to pericardium segmentation and epicardial fat volume (EFV) estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a random forest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated computer tomography angiography volumes shows a significant improvement on state-of-the-art in terms of EFV estimation [mean absolute EFV difference: 3.8 ml (4.7%), Pearson correlation: 0.99] with run times suitable for large-scale studies (52 s). Further, the results compare favorably with interobserver variability measured on 10 volumes.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
computed tomography angiography, epicardial fat quantification, machine learning, pericardium, segmentation
in
Journal of Medical Imaging
volume
3
issue
3
publisher
SPIE
external identifiers
  • scopus:84988978052
  • wos:000388232100008
ISSN
2329-4302
DOI
10.1117/1.JMI.3.3.034003
language
English
LU publication?
yes
id
e297b0e2-70e2-4e8a-9120-baf03bd80b75
date added to LUP
2017-01-24 13:15:34
date last changed
2017-09-18 11:33:38
@article{e297b0e2-70e2-4e8a-9120-baf03bd80b75,
  abstract     = {<p>Recent findings indicate a strong correlation between the risk of future heart disease and the volume of adipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delineation of the pericardium is extremely time-consuming and that existing methods for automatic delineation lack accuracy. An efficient and fully automatic approach to pericardium segmentation and epicardial fat volume (EFV) estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a random forest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated computer tomography angiography volumes shows a significant improvement on state-of-the-art in terms of EFV estimation [mean absolute EFV difference: 3.8 ml (4.7%), Pearson correlation: 0.99] with run times suitable for large-scale studies (52 s). Further, the results compare favorably with interobserver variability measured on 10 volumes.</p>},
  articleno    = {034003},
  author       = {Norlén, Alexander and Alvén, Jennifer and Molnar, David and Enqvist, Olof and Norrlund, Rauni Rossi and Brandberg, John and Bergström, Göran and Kahl, Fredrik},
  issn         = {2329-4302},
  keyword      = {computed tomography angiography,epicardial fat quantification,machine learning,pericardium,segmentation},
  language     = {eng},
  month        = {07},
  number       = {3},
  publisher    = {SPIE},
  series       = {Journal of Medical Imaging},
  title        = {Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography},
  url          = {http://dx.doi.org/10.1117/1.JMI.3.3.034003},
  volume       = {3},
  year         = {2016},
}