Towards Accurate and Fast Clinical Body Composition: A Resource-Efficient Hierarchical Segmentation Framework for Multi-Source CT

2026-07-08

AI Summary

This study addresses the challenges posed by heterogeneity across multi-source CT data and high memory demands that hinder the clinical deployment of automated muscle and adipose tissue segmentation. To overcome these limitations, the authors propose a resource-efficient, coarse-to-fine hierarchical segmentation framework that innovatively integrates dynamic spacing, anisotropic patching, grouped-inference sliding windows, and topology-aware asymmetric resampling, enabling highly efficient CPU-based inference without requiring a GPU. In an independent test cohort of 105 cases, the method achieved Dice scores ranging from 0.924 to 0.982 across ten tissue classes, with eight meeting the clinical requirement of ±10% volumetric error. Each case was processed in just 44.5 seconds, with a peak memory footprint as low as 4.73 GB.

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.

Problem

• body composition
• CT segmentation
• data heterogeneity
• memory efficiency
• clinical deployment

Innovation

• Hierarchical Segmentation
• Dynamic Spacing
• Anisotropic Patching
• Group Inference
• Topology-Aware Resampling