AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes.
Authors
Affiliations (7)
Affiliations (7)
- Auckland Bioengineering Institute, The University of Auckland, 70 Symonds Street, Auckland, 1010, New Zealand.
- Department of Computer Science, University of Oxford, Oxford, UK.
- Faculty of Medical and Health Sciences, School of Medicine, The University of Auckland, Auckland, New Zealand.
- Department of Engineering Science and Biomedical Engineering, The University of Auckland, Auckland, New Zealand.
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
- Department of Clinical Physiology in Linköping, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
- Auckland Bioengineering Institute, The University of Auckland, 70 Symonds Street, Auckland, 1010, New Zealand. [email protected].
Abstract
Epicardial adipose tissue (EAT) is associated with cardiometabolic risk in type 2 diabetes (T2D), but its spatial distribution and structural alterations remain understudied. We aim to develop a shape-aware, AI-based method for automated segmentation and morphogeometric analysis of EAT in T2D. A total of 90 participants (45 with T2D and 45 age-, sex-matched controls) underwent cardiac 3D Dixon MRI, enrolled between 2014 and 2018 as part of the sub-study of the Swedish SCAPIS cohort. We developed EAT-Seg, a multi-modal deep learning model incorporating signed distance maps (SDMs) for shape-aware segmentation. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), the 95% Hausdorff distance (HD95), and the average symmetric surface distance (ASSD). Statistical shape analysis combined with partial least squares discriminant analysis (PLS-DA) was applied to point cloud representations of EAT to capture latent spatial variations between groups. Morphogeometric features, including volume, 3D local thickness map, elongation and fragmentation index, were extracted and correlated with PLS-DA latent variables using Pearson correlation. Features with high-correlation were identified as key differentiators and evaluated using a Random Forest classifier. EAT-Seg achieved a DSC of 0.881, a HD95 of 3.213 mm, and an ASSD of 0.602 mm. Statistical shape analysis revealed spatial distribution differences in EAT between T2D and control groups. Morphogeometric feature analysis identified volume and thickness gradient-related features as key discriminators (r > 0.8, P < 0.05). Random Forest classification achieved an AUC of 0.703. This AI-based framework enables accurate segmentation for structurally complex EAT and reveals key morphogeometric differences associated with T2D, supporting its potential as a biomarker for cardiometabolic risk assessment.