An enhanced deep learning method for the quantification of epicardial adipose tissue

Epicardial adipose tissue (EAT) significantly contributes to the progression of cardiovascular diseases (CVDs). However, manually quantifying EAT volume is labor-intensive and susceptible to human error. Although there have been some deep learning-based methods for automatic quantification of EAT, t...

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Bibliographic Details
Published in:Scientific reports 2024-10, Vol.14 (1), p.24947-14, Article 24947
Main Authors: Tang, Ke-Xin, Liao, Xiao-Bo, Yuan, Ling-Qing, He, Sha-Qi, Wang, Min, Mei, Xi-Long, Zhou, Zhi-Ang, Fu, Qin, Lin, Xiao, Liu, Jun
Format: Article
Language:English
Subjects:
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Adipose tissue
Adipose Tissue - diagnostic imaging
Aged
Angiography
Body fat
Cardiovascular diseases
Computed tomography
Computed Tomography Angiography - methods
Coronary Angiography - methods
Coronary computed tomography angiography (CCTA)
Deep Learning
Epicardial Adipose Tissue
Female
Humanities and Social Sciences
Humans
Male
Middle Aged
multidisciplinary
Pericardium
Pericardium - diagnostic imaging
Post-processing
Science
Science (multidisciplinary)
Segmentation
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Summary:Epicardial adipose tissue (EAT) significantly contributes to the progression of cardiovascular diseases (CVDs). However, manually quantifying EAT volume is labor-intensive and susceptible to human error. Although there have been some deep learning-based methods for automatic quantification of EAT, they are mostly uninterpretable and fail to harness the complete anatomical characteristics. In this study, we proposed an enhanced deep learning method designed for EAT quantification on coronary computed tomography angiography (CCTA) scan, which integrated both data-driven method and specific morphological information. A total of 108 patients who underwent routine CCTA examinations were included in this study. They were randomly assigned to training set ( n  = 60), validation set ( n  = 8), and test set ( n  = 40). We quantified and calculated the EAT volume based on the CT attenuation values within the predicted pericardium. The automatic method demonstrated strong agreement with expert manual quantification, yielding a median Dice score coefficients (DSC) of 0.916 (Interquartile Range (IQR): 0.846–0.948) for 2D slices. Meanwhile, the median DSC for the 3D volume was 0.896 (IQR: 0.874–0.908) between these two measures, with an excellent correlation of 0.980 ( p  
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-75659-9