Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography
(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
- Norlén, Alexander ; Alvén, Jennifer ; Molnar, David ; Enqvist, Olof ; Norrlund, Rauni Rossi ; Brandberg, John ; Bergström, Göran and Kahl, Fredrik LU
- organization
- publishing date
- 2016-07-01
- 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
- article number
- 034003
- publisher
- SPIE
- external identifiers
-
- scopus:84988978052
- pmid:27660804
- 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
- 2024-11-17 15:29:24
@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>}},
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}},
keywords = {{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}},
doi = {{10.1117/1.JMI.3.3.034003}},
volume = {{3}},
year = {{2016}},
}