# AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes

## TL;DR

This paper introduces an AI method to accurately segment and analyze epicardial fat in type 2 diabetes patients, revealing structural differences linked to cardiometabolic risk.

## Contribution

A novel shape-aware AI framework for EAT segmentation and morphogeometric profiling in T2D, identifying key structural biomarkers.

## Key findings

- EAT-Seg achieved high segmentation accuracy (DSC 0.881, HD95 3.213 mm, ASSD 0.602 mm).
- Morphogeometric features like volume and thickness gradients were strong discriminators between T2D and controls (r > 0.8, P < 0.05).
- Random Forest classification reached an AUC of 0.703 for T2D detection using these features.

## 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.

The online version contains supplementary material available at 10.1186/s12933-025-02829-y.