National Institutes of Health (NIH) Clinical Center, Bethesda, MD, USA.
Walter Reed National Military Medical Center, Bethesda, MD, USA.
Int J Comput Assist Radiol Surg. 2024 Aug;19(8):1589-1596. doi: 10.1007/s11548-024-03167-2. Epub 2024 May 17.
Body composition measurements from routine abdominal CT can yield personalized risk assessments for asymptomatic and diseased patients. In particular, attenuation and volume measures of muscle and fat are associated with important clinical outcomes, such as cardiovascular events, fractures, and death. This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool.
We assessed the tools across 900 CT series from the publicly available SAROS dataset, focusing on muscle, subcutaneous fat, and visceral fat. The Dice score was employed to assess accuracy in subcutaneous fat and muscle segmentation. Due to the lack of ground truth segmentations for visceral fat, Cohen's Kappa was utilized to assess segmentation agreement between the tools.
Our Internal tool achieved a 3% higher Dice (83.8 vs. 80.8) for subcutaneous fat and a 5% improvement (87.6 vs. 83.2) for muscle segmentation, respectively. A Wilcoxon signed-rank test revealed that our results were statistically different with p < 0.01. For visceral fat, the Cohen's Kappa score of 0.856 indicated near-perfect agreement between the two tools. Our internal tool also showed very strong correlations for muscle volume (R =0.99), muscle attenuation (R =0.93), and subcutaneous fat volume (R =0.99) with a moderate correlation for subcutaneous fat attenuation (R =0.45).
Our findings indicated that our Internal tool outperformed TotalSegmentator in measuring subcutaneous fat and muscle. The high Cohen's Kappa score for visceral fat suggests a reliable level of agreement between the two tools. These results demonstrate the potential of our tool in advancing the accuracy of body composition analysis.
常规腹部 CT 的身体成分测量可为无症状和患病患者提供个性化的风险评估。特别是，肌肉和脂肪的衰减和体积测量与重要的临床结果相关，如心血管事件、骨折和死亡。本研究评估了一种内部工具（用于肌肉和脂肪（皮下和内脏）分割的工具）与成熟的公共 TotalSegmentator 工具相比的可靠性。
我们在公开的 SAROS 数据集的 900 个 CT 系列中评估了这些工具，重点是肌肉、皮下脂肪和内脏脂肪。使用 Dice 评分评估皮下脂肪和肌肉分割的准确性。由于缺乏内脏脂肪的真实分割，因此使用 Cohen's Kappa 评估工具之间的分割一致性。
我们的内部工具分别在皮下脂肪分割方面提高了 3%的 Dice 分数（83.8 对 80.8），在肌肉分割方面提高了 5%（87.6 对 83.2）。Wilcoxon 符号秩检验表明，我们的结果具有统计学差异，p<0.01。对于内脏脂肪，两个工具之间的 Cohen's Kappa 评分 0.856 表明几乎完全一致。我们的内部工具还显示了肌肉体积（R=0.99）、肌肉衰减（R=0.93）和皮下脂肪体积（R=0.99）的非常强的相关性，而皮下脂肪衰减（R=0.45）的相关性中等。
我们的发现表明，我们的内部工具在测量皮下脂肪和肌肉方面优于 TotalSegmentator。内脏脂肪的高 Cohen's Kappa 评分表明两个工具之间具有可靠的一致性水平。这些结果表明我们的工具在提高身体成分分析的准确性方面具有潜力。
