Existing automated methods for Epicardial Adipose Tissue (EAT) segmentation in cardiac CT lack systematic validation across heterogeneous adiposity phenotypes and rarely report reproducibility against human observer standards. We present a 3D U-Net trained on 130 calcium scoring CT scans acquired on a Philips Brilliance 16-slice CT scanner from the PESA (Progression of Early Subclinical Atherosclerosis) CNIC-SANTANDER study, stratified by EAT volume into three categories (low, medium, high) across training (100) and validation (30) sets. Images were preprocessed with a Hounsfield Unit window with width 500 and level 50. The network was optimised with AdamW, cosine annealing, and a combined Dice-Focal loss. Augmentation included random affine transformations and Gaussian smoothing. Inference used sliding window with overlap. The model was benchmarked against SwinUNETR and a 3D U-Net with attention gates on the same dataset. Reproducibility was assessed on two independent test set (20 each): one for intra-observer and another one for inter-observer analysis, both providing ICC, Passing–Bablok regression and Bland-Altman.
On the test set, the 3D U-Net achieved the highest Dice (0.795) and lowest volume error (10.7%) among all architectures. SwinUNETR underperformed due to the tension between its global attention mechanism and patch-based inference, amplified by the limited training set size. The attention-gate variant showed the lowest Dice (0.750) and highest HD95 (35.17 mm), suggesting architectural complexity may hinder convergence with small datasets. Despite being the most compact architecture evaluated, the 3D U-Net achieved reproducibility within human observer range. In the reproducibility study, human inter-observer ICC = 0.979 (0.855; 0.994) with Bias = 2.45 cm³. The AI model achieved ICC = 0.987 and Bias = 1.56 cm³, falling within the inter-observer range and confirming comparable agreement to human experts. These results position the proposed pipeline as a robust and scalable alternative to manual EAT annotation in large-scale CT studies.