无需GPU，44秒完成一例体成分分析，精度达临床标准。
Towards Accurate and Fast Clinical Body Composition: A Resource-Efficient Hierarchical Segmentation Framework for Multi-Source CT
- 分层粗到细分割十类组织，动态间距与各向异性切片降内存
- 单例处理仅需44.5秒，峰值内存4.73GB，满足临床部署要求
- 适用于普通电脑的大型体成分分析，适合医学影像研究者
自动化三维CT图像中肌肉与脂肪组织的分割对体成分分析至关重要，但多源数据异质性及高内存消耗限制了其临床应用。本文提出一种粗到细的分层框架，用于分割十类组织结构。通过动态间距与各向异性切片优化效率，采用组推理机制实现低内存滑动窗口处理，并使用拓扑感知非对称重采样加速后处理。模型在来自七个公开和两个私有数据集的1,558例CT图像上训练，在独立测试集（N=105）上，各结构的骰子系数介于0.924至0.982之间，八类主要组织满足±10%相对误差的临床接受标准。在12核CPU工作站上，该无GPU流水线平均耗时44.5秒/例，峰值内存为4.73 GB。结果表明，该框架在准确性和效率间取得良好平衡，可在标准CPU工作站上实现鲁棒的大规模体成分分析。
原文摘要 · Abstract (English)
Background: Automated 3D segmentation of muscles and adipose tissue from CT is vital for body composition analysis, but multi-source data heterogeneity and high CPU memory demands hinder clinical deployment. Methods: We propose a coarse-to-fine hierarchical framework to segment ten tissue structures. Efficiency is optimized using Dynamic Spacing and Anisotropic Patching, a Group Inference mechanism for low-memory sliding-window processing, and Topology-Aware Asymmetric Resampling for fast post-processing. Results: The framework was trained on 1,558 CT volumes from seven public and two private datasets, and evaluated on an independent test cohort (N=105), per-structure Dice coefficients ranged from 0.924 to 0.982. Eight major structures met the +-10% relative error clinical acceptance limit. On a 12-core CPU workstation, the GPU-free pipeline averaged 44.5 seconds per volume with 4.73 GB peak memory. Conclusion: This framework balances accuracy and efficiency, enabling robust, large-scale body composition analysis on standard CPU workstations.
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