{"ID":6138165,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T09:01:53.812435343Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07177","arxiv_id":"2607.07177","title":"Towards Accurate and Fast Clinical Body Composition: A Resource-Efficient Hierarchical Segmentation Framework for Multi-Source CT","abstract":"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.","short_abstract":"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 opti...","url_abs":"https://arxiv.org/abs/2607.07177","url_pdf":"https://arxiv.org/pdf/2607.07177v1","authors":"[\"Xiaodi Shen\",\"Qingzhu Zheng\",\"Yaoyang Qiu\",\"Cien Fan\",\"Ruonan Zhang\",\"Yangdi Wang\",\"Luyao Wu\",\"Weikai Zheng\",\"Longfei Zhao\",\"Bing Li\",\"Rulin Xu\",\"Qiqi Xu\",\"Ren Mao\",\"Shiting Feng\",\"Xuehua Li\"]","published":"2026-07-08T09:11:41Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[]","has_code":false}