用注意力机制提升肝脏与脂肪组织分割精度
Attention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Images
- 在GhostUNet++中引入通道、空间和深度注意力机制
- 在两个数据集上达到最高96.5%的分割Dice系数
- 适合医学图像分析与体成分研究者使用
准确分割腹部脂肪组织（皮下脂肪SAT和内脏脂肪VAT）及肝脏，对理解体成分及其相关健康风险（如2型糖尿病和心血管疾病）至关重要。本文提出Attention GhostUNet++，一种新型深度学习模型，在Ghost UNet++瓶颈处融合通道、空间和深度注意力机制，实现自动精准分割。在AATTCT-IDS和LiTS数据集上评估，VAT分割Dice系数达0.9430，SAT为0.9639，肝脏为0.9652，优于基线模型。尽管边界细节分割仍有小幅不足，但该模型显著提升了特征提炼、上下文理解与计算效率，为体成分分析提供稳健解决方案。代码已开源：https://github.com/MansoorHayat777/Attention-GhostUNetPlusPlus。
原文摘要 · Abstract (English)
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.
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