Accurate segmentation of abdominal adipose tissue, including subcutaneous (SAT) and visceral adipose tissue (VAT), along with liver segmentation, is essential for understanding body composition and associated health risks such as type 2 diabetes and cardiovascular disease. This study proposes Attention GhostUNet++, a novel deep learning model incorporating Channel, Spatial, and Depth Attention mechanisms into the Ghost UNet++ bottleneck for automated, precise segmentation. Evaluated on the AATTCT-IDS and LiTS datasets, the model achieved Dice coefficients of 0.9430 for VAT, 0.9639 for SAT, and 0.9652 for liver segmentation, surpassing baseline models. Despite minor limitations in boundary detail segmentation, the proposed model significantly enhances feature refinement, contextual understanding, and computational efficiency, offering a robust solution for body composition analysis. The implementation of the proposed Attention GhostUNet++ model is available at:this https URL.
View on arXiv@article{hayat2025_2504.11491,
title={ Attention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Images },
author={ Mansoor Hayat and Supavadee Aramvith and Subrata Bhattacharjee and Nouman Ahmad },
journal={arXiv preprint arXiv:2504.11491},
year={ 2025 }
}