Attention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Images

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: https://github.com/MansoorHayat777/Attention-GhostUNetPlusPlus.Clinical relevance- The Attention GhostUNet++ model offers a significant advancement in the automated segmentation of adipose tissue and liver regions from CT images. Accurate delineation of visceral and subcutaneous adipose tissue, alongside liver structures, is critical for clinicians managing cardiometabolic disorders, including type 2 diabetes and cardiovascular diseases. By reducing reliance on manual annotations, the model enhances efficiency and scalability, paving the way for its integration into routine clinical workflows and large-scale body composition studies.