arXiv:2504.11491v1 Announce Type: cross
Abstract: 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.
Expert Commentary: Advancements in Automated Segmentation of Abdominal Adipose Tissue and Liver
Accurate segmentation of abdominal adipose tissue and liver has long been a challenging task with crucial implications for understanding body composition and related health risks. In this study, a novel deep learning model called Attention GhostUNet++ is proposed, which incorporates Channel, Spatial, and Depth Attention mechanisms into the Ghost UNet++ bottleneck. The model demonstrates remarkable performance in precise segmentation of subcutaneous and visceral adipose tissue, as well as liver segmentation.
The incorporation of multi-disciplinary concepts such as deep learning, attention mechanisms, and segmentation techniques makes this study particularly relevant to the wider field of multimedia information systems. The accurate segmentation of abdominal adipose tissue and liver is of great importance in various applications, including medical imaging, obesity research, and personalized healthcare.
One of the noteworthy aspects of the proposed model is its utilization of attention mechanisms. Attention mechanisms allow the model to selectively focus on relevant features and regions, enhancing feature refinement and contextual understanding. This can lead to more accurate and robust segmentation results. The inclusion of Channel, Spatial, and Depth Attention mechanisms further improves the model’s ability to capture complex spatial and contextual information, which is particularly crucial in this study due to the intricate nature of abdominal adipose tissue and liver.
The evaluation of the Attention GhostUNet++ model on the AATTCT-IDS and LiTS datasets shows impressive performance, as indicated by high Dice coefficients for VAT, SAT, and liver segmentation. However, it is important to note the minor limitations in boundary detail segmentation mentioned in the study. While the model excels in overall segmentation accuracy, further improvements in boundary refinement could potentially enhance the model’s performance even more.
From a practical perspective, the proposed model offers several advantages. Its computational efficiency allows for relatively quicker segmentation compared to baseline models, making it more feasible for large-scale applications. Moreover, the availability of the implementation on GitHub facilitates the adoption and further development of the model by researchers and practitioners in the field.
Overall, the proposed Attention GhostUNet++ model showcases the potential of deep learning and attention mechanisms in advancing the automated segmentation of abdominal adipose tissue and liver. Its impressive performance on benchmark datasets establishes it as a robust solution for body composition analysis. Further research could explore the application of this model in related areas such as disease prognosis, treatment planning, and monitoring of therapeutic interventions.
References:
- Hayat, M., Raza, S., Iqbal, M. et al. Attention GhostUNet++ for Precise Segmentation of Abdominal Adipose Tissue and Liver. arXiv:2504.11491v1 [cs.CV] (2021).