Winder Christopher, Clark Matthew, Frood Russell, Smith Lesley, Bulpitt Andrew, Cook Gordon, Scarsbrook Andrew
UKRI CDT in AI for Medical Diagnosis and Care, University of Leeds, Woodhouse, LS2 9JT, Leeds, UK; School of Computing, University of Leeds, Woodhouse, LS2 9JT, Leeds, UK.
Department of Radiology, St.James University Hospital, Beckett St, Harehills, LS9 7TF, Leeds, UK.
Eur J Radiol. 2024 Dec;181:111764. doi: 10.1016/j.ejrad.2024.111764. Epub 2024 Sep 30.
To review methodological approaches for automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and skeletal muscle from abdominal cross-sectional imaging for body composition analysis.
Four databases were searched for publications describing automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and/or skeletal muscle from abdominal CT or MR imaging between 2019 and 2023. Included reports were evaluated to assess how imaging modality, cohort size, vertebral level, model dimensionality, and use of a volume or single slice affected segmentation accuracy and/or clinical utility. Exclusion criteria included reports not in English language, manual or semi-automated segmentation methods, reports prior to 2019 or solely of paediatric patients, and those not describing the use of abdominal CT or MR.
After exclusions, 172 reports were included in the review. CT imaging was utilised approximately four times as often as MRI, and segmentation accuracy did not significantly differ between the two modalities. Cohort size had no significant effect on segmentation accuracy. There was little evidence to refute the current practice of extracting body composition metrics from the third lumbar vertebral level. There was no clear benefit of using a 3D model to perform segmentation over a 2D approach.
Automated segmentation of intra-abdominal soft tissues for body composition analysis is an intense area of research activity. Segmentation accuracy is not affected by cross-sectional imaging modality. Extracting metrics from a single slice at the third lumbar vertebral level is a common approach, however, extracting metrics from a volumetric slab surrounding this level may increase the resilience of the technique, which is important for clinical translation. A paucity of publicly available datasets led to most reports using different data sources, preventing direct comparison of segmentation techniques. Future efforts should prioritise creating a standardised dataset to facilitate benchmarking of different algorithms and subsequent clinical adoption.
回顾从腹部横断面成像中自动分割皮下脂肪组织、内脏脂肪组织和骨骼肌以进行身体成分分析的方法。
检索了四个数据库，以查找2019年至2023年间描述从腹部CT或MR成像中自动分割皮下脂肪组织、内脏脂肪组织和/或骨骼肌的出版物。对纳入的报告进行评估，以评估成像方式、队列规模、椎体水平、模型维度以及使用体积数据还是单一层面数据如何影响分割准确性和/或临床实用性。排除标准包括非英文报告、手动或半自动分割方法、2019年之前的报告或仅涉及儿科患者的报告，以及未描述使用腹部CT或MR的报告。
排除后，172篇报告纳入本综述。CT成像的使用频率约为MRI的四倍，两种成像方式的分割准确性无显著差异。队列规模对分割准确性无显著影响。几乎没有证据反驳从第三腰椎水平提取身体成分指标的现行做法。与二维方法相比，使用三维模型进行分割没有明显优势。
用于身体成分分析的腹内软组织自动分割是一个活跃的研究领域。分割准确性不受横断面成像方式的影响。从第三腰椎水平的单一层面提取指标是一种常见方法，然而，从该水平周围的体积块提取指标可能会提高该技术的适应性，这对临床转化很重要。公开可用数据集的匮乏导致大多数报告使用不同的数据源，妨碍了分割技术的直接比较。未来的工作应优先创建标准化数据集，以促进不同算法的基准测试及后续临床应用。
