Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies
Journal article, 2021
To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.90 when tested against expert manual segmentations on 25 image sets. Tested on 1400 image sets, the model successfully segmented 99.4% of the cases. Automatic imputation of missing EAT volumes had an error of less than 3.1% with up to 20% of the slices in image sets missing. The most important predictors of EAT volumes were weight and waist, while EAT attenuation was predicted mainly by EAT volume. A model with excellent performance, capable of fully automatic handling of the most common challenges in large scale EAT quantification has been developed. In studies of the importance of EAT in disease development, the strong co-variation with anthropometric measures needs to be carefully considered.
Author
David Molnar
University of Gothenburg
Sahlgrenska University Hospital
Olof Enqvist
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Eigenvision AB
Johannes Ulén
Eigenvision AB
Måns Larsson
Eigenvision AB
John Brandberg
Sahlgrenska University Hospital
University of Gothenburg
Åse (Allansdotter) Johnsson
University of Gothenburg
Elias Björnson
University of Gothenburg
Göran Bergström
Sahlgrenska University Hospital
University of Gothenburg
Ola Hjelmgren
University of Gothenburg
Sahlgrenska University Hospital
Scientific Reports
2045-2322 (ISSN) 20452322 (eISSN)
Vol. 11 1 23905Subject Categories (SSIF 2011)
Computer Vision and Robotics (Autonomous Systems)
Medical Image Processing
DOI
10.1038/s41598-021-03150-w
PubMed
34903773