Background: Epicardial adipose tissue (EAT) is a key aspect in the investigation of cardiac pathophysiology. We sought to develop a deep learning (DL) model for fully automatic extraction and quantification of EAT through pulmonary computed tomography venography (PCTV) images.
Methods: In this retrospective study, we included 128 patients with atrial fibrillation and PCTV from 2 hospitals. A DL model for automated EAT segmentation was developed from a training set of 51 patients and a validation set of 13 patients from hospital A. The algorithm was further validated using an internal test set of 16 patients from hospital A and an external test set of 48 patients from hospital B. The consistency and measurement agreement of EAT quantification were compared between the DL model and the conventional manual protocol using the Dice score coefficient (DSC), Hausdorff distance (HD95), Pearson correlation coefficient, and Bland-Altman plot.
Results: In the internal and external test set, automated segmentation with DL was successful in all cases. The total analysis time was shorter for DL than for manual reconstruction (5.43±2.52 vs. 106.20±15.90 min; P<0.001). The EAT segmented with the DL model had good consistency with manual segmentation (the DSC of the internal and external test sets were 0.92±0.02 and 0.88±0.03, respectively). The quantification of EAT evaluated with the 2 methods showed excellent correlation (all correlation coefficients >0.9; all P values <0.001) and minimal measurement difference.
Conclusions: The proposed DL model achieved fully automatic quantification of EAT from PCTV images. The yielded results were highly consistent with those of manual quantification.
Keywords: Epicardial adipose tissue (EAT); deep learning (DL); pulmonary computed tomography venography (PCTV).
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