Attention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Images
This work addresses the need for precise segmentation to understand health risks like type 2 diabetes and cardiovascular disease, offering an incremental improvement over existing methods.
The study tackled the problem of accurate segmentation of abdominal adipose tissue and liver in CT images for body composition analysis, achieving Dice coefficients of 0.9430 for visceral adipose tissue, 0.9639 for subcutaneous adipose tissue, and 0.9652 for liver segmentation, surpassing baseline models.
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.