ArticleScientific reports2021
Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 15 citations in OpenAlex.
- Fine-grained epicardial adipose tissue segmentation in cardiac CT images with position priors and edge enhancement.Biomedical engineering letters · 2026Article
- Deep learning-based quantification of epicardial adipose tissue volume from non-contrast computed tomography images: a multi-centre study.European heart journal. Digital health · 2025Article
- Chest CT-based analysis of radiomic and volumetric differences in epicardial adipose tissue in HFrEF patients with and without AF.BMC cardiovascular disorders · 2025Article
- Coronary artery calcifications are not associated with epicardial adipose tissue volume and attenuation on computed tomography in 1,945 individuals with various degrees of glucose disorders.International journal of cardiology. Heart & vasculature · 2025Article
- Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.JACC. Cardiovascular imaging · 2023Article
- Pre-diabetes is associated with attenuation rather than volume of epicardial adipose tissue on computed tomography.Scientific reports · 2023Article
- Feasibility of the fat-suppression image-subtraction method using deep learning for abnormality detection on knee MRI.Polish journal of radiology · 2023Article
- Discussion on the possibility of multi-layer intelligent technologies to achieve the best recover of musculoskeletal injuries: Smart materials, variable structures, and intelligent therapeutic planning.Frontiers in bioengineering and biotechnology · 2022Review
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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