AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes
Abstract Background 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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
|
| Series: | Cardiovascular Diabetology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12933-025-02829-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849238030508883968 |
|---|---|
| author | Fan Feng Abdallah I. Hasaballa Ting Long Xinyi Sun Justin Fernandez Carl-Johan Carlhäll Jichao Zhao |
| author_facet | Fan Feng Abdallah I. Hasaballa Ting Long Xinyi Sun Justin Fernandez Carl-Johan Carlhäll Jichao Zhao |
| author_sort | Fan Feng |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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. Conclusions 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. Graphical abstract |
| format | Article |
| id | doaj-art-e5b7f68121e143f4b720447f852eb3df |
| institution | Kabale University |
| issn | 1475-2840 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Cardiovascular Diabetology |
| spelling | doaj-art-e5b7f68121e143f4b720447f852eb3df2025-08-20T04:01:47ZengBMCCardiovascular Diabetology1475-28402025-07-0124111410.1186/s12933-025-02829-yAI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetesFan Feng0Abdallah I. Hasaballa1Ting Long2Xinyi Sun3Justin Fernandez4Carl-Johan Carlhäll5Jichao Zhao6Auckland Bioengineering Institute, The University of AucklandDepartment of Computer Science, University of OxfordAuckland Bioengineering Institute, The University of AucklandFaculty of Medical and Health Sciences, School of Medicine, The University of AucklandAuckland Bioengineering Institute, The University of AucklandCenter for Medical Image Science and Visualization (CMIV), Linköping UniversityAuckland Bioengineering Institute, The University of AucklandAbstract Background 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. Methods 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. Results 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. Conclusions 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. Graphical abstracthttps://doi.org/10.1186/s12933-025-02829-yEpicardial adipose tissueType 2 diabetesMRIMulti-modal deep learningStatistical shape analysis |
| spellingShingle | Fan Feng Abdallah I. Hasaballa Ting Long Xinyi Sun Justin Fernandez Carl-Johan Carlhäll Jichao Zhao AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes Cardiovascular Diabetology Epicardial adipose tissue Type 2 diabetes MRI Multi-modal deep learning Statistical shape analysis |
| title | AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes |
| title_full | AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes |
| title_fullStr | AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes |
| title_full_unstemmed | AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes |
| title_short | AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes |
| title_sort | ai driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes |
| topic | Epicardial adipose tissue Type 2 diabetes MRI Multi-modal deep learning Statistical shape analysis |
| url | https://doi.org/10.1186/s12933-025-02829-y |
| work_keys_str_mv | AT fanfeng aidrivensegmentationandmorphogeometricprofilingofepicardialadiposetissueintype2diabetes AT abdallahihasaballa aidrivensegmentationandmorphogeometricprofilingofepicardialadiposetissueintype2diabetes AT tinglong aidrivensegmentationandmorphogeometricprofilingofepicardialadiposetissueintype2diabetes AT xinyisun aidrivensegmentationandmorphogeometricprofilingofepicardialadiposetissueintype2diabetes AT justinfernandez aidrivensegmentationandmorphogeometricprofilingofepicardialadiposetissueintype2diabetes AT carljohancarlhall aidrivensegmentationandmorphogeometricprofilingofepicardialadiposetissueintype2diabetes AT jichaozhao aidrivensegmentationandmorphogeometricprofilingofepicardialadiposetissueintype2diabetes |