Literature DB >> 21400320

Towards automatic quantification of the epicardial fat in non-contrasted CT images.

Jorge G Barbosa1, Bruno Figueiredo, Nuno Bettencourt, João Manuel R S Tavares.   

Abstract

In this work, we present a technique to semi-automatically quantify the epicardial fat in non-contrasted computed tomography (CT) images. The epicardial fat is very close to the pericardial fat, being separated only by the pericardium that appears in the image as a very thin line, which is hard to detect. Therefore, an algorithm that uses the anatomy of the heart was developed to detect the pericardium line via control points of the line. From the points detected an interpolation was applied based on the cubic interpolation, which was also improved to avoid incorrect interpolation that occurs when the two variables are non-monotonic. The method is validated by using a set of 40 CT images of the heart of 40 human subjects. In 62.5% of the cases only minimal user intervention was required and the results compared favourably with the results obtained by the manual process.

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Year:  2011        PMID: 21400320     DOI: 10.1080/10255842.2010.499871

Source DB:  PubMed          Journal:  Comput Methods Biomech Biomed Engin        ISSN: 1025-5842            Impact factor:   1.763


  8 in total

1.  The correlation of epicardial adipose tissue on postmortem CT with coronary artery stenosis as determined by autopsy.

Authors:  Damien I Sequeira; Lars C Ebert; Patricia M Flach; Thomas D Ruder; Michael J Thali; Garyfalia Ampanozi
Journal:  Forensic Sci Med Pathol       Date:  2015-02-25       Impact factor: 2.007

2.  Association of left ventricular mechanical dyssynchrony with survival benefit from revascularization: a study of gated positron emission tomography in patients with ischemic LV dysfunction and narrow QRS.

Authors:  Wael AlJaroudi; M Chadi Alraies; Rory Hachamovitch; Wael A Jaber; Richard Brunken; Manuel D Cerqueira; Thomas Marwick
Journal:  Eur J Nucl Med Mol Imaging       Date:  2012-06-15       Impact factor: 9.236

Review 3.  [Epicardial fat: Imaging and implications for diseases of the cardiovascular system].

Authors:  M Niemann; H Alkadhi; A Gotschy; S Kozerke; R Manka
Journal:  Herz       Date:  2014-09-03       Impact factor: 1.443

Review 4.  Novel imaging biomarkers: epicardial adipose tissue evaluation.

Authors:  Caterina B Monti; Marina Codari; Carlo Nicola De Cecco; Francesco Secchi; Francesco Sardanelli; Arthur E Stillman
Journal:  Br J Radiol       Date:  2019-12-11       Impact factor: 3.039

5.  Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT.

Authors:  Frederic Commandeur; Markus Goeller; Julian Betancur; Sebastien Cadet; Mhairi Doris; Xi Chen; Daniel S Berman; Piotr J Slomka; Balaji K Tamarappoo; Damini Dey
Journal:  IEEE Trans Med Imaging       Date:  2018-02-09       Impact factor: 10.048

6.  Semiautomatic Epicardial Fat Segmentation Based on Fuzzy c-Means Clustering and Geometric Ellipse Fitting.

Authors:  Vladimir Zlokolica; Lidija Krstanović; Lazar Velicki; Branislav Popović; Marko Janev; Ratko Obradović; Nebojsa M Ralević; Ljubomir Jovanov; Danilo Babin
Journal:  J Healthc Eng       Date:  2017-09-20       Impact factor: 2.682

7.  Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies.

Authors:  David Molnar; Olof Enqvist; Johannes Ulén; Måns Larsson; John Brandberg; Åse A Johnsson; Elias Björnson; Göran Bergström; Ola Hjelmgren
Journal:  Sci Rep       Date:  2021-12-13       Impact factor: 4.379

8.  Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study.

Authors:  Frederic Commandeur; Markus Goeller; Aryabod Razipour; Sebastien Cadet; Michaela M Hell; Jacek Kwiecinski; Xi Chen; Hyuk-Jae Chang; Mohamed Marwan; Stephan Achenbach; Daniel S Berman; Piotr J Slomka; Balaji K Tamarappoo; Damini Dey
Journal:  Radiol Artif Intell       Date:  2019-11-27
  8 in total

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