Department of Radiology, Uppsala University, Uppsala, Sweden.
Antaros Medical, BioVenture Hub, Mölndal, Sweden.
Sci Rep. 2017 Sep 5;7(1):10425. doi: 10.1038/s41598-017-08925-8.
Computed Tomography (CT) allows detailed studies of body composition and its association with metabolic and cardiovascular disease. The purpose of this work was to develop and validate automated and manual image processing techniques for detailed and efficient analysis of body composition from CT data. The study comprised 107 subjects examined in the Swedish CArdioPulmonary BioImage Study (SCAPIS) using a 3-slice CT protocol covering liver, abdomen, and thighs. Algorithms were developed for automated assessment of liver attenuation, visceral (VAT) and subcutaneous (SAT) abdominal adipose tissue, thigh muscles, subcutaneous, subfascial (SFAT) and intermuscular adipose tissue. These were validated using manual reference measurements. SFAT was studied in selected subjects were the fascia lata could be visually identified (approx. 5%). In addition, precision of manual measurements of intra- (IPAT) and retroperitoneal adipose tissue (RPAT) and deep- and superficial SAT was evaluated using repeated measurements. Automated measurements correlated strongly to manual reference measurements. The SFAT depot showed the weakest correlation (r = 0.744). Automated VAT and SAT measurements were slightly, but significantly overestimated (≤4.6%, p ≤ 0.001). Manual segmentation of abdominal sub-depots showed high repeatability (CV ≤ 8.1%, r ≥ 0.930). We conclude that the low dose CT-scanning and automated analysis makes the setup suitable for large-scale studies.
计算机断层扫描（CT）可以对人体成分进行详细研究，并将其与代谢和心血管疾病联系起来。本研究旨在开发和验证自动化和手动图像处理技术，以便从 CT 数据中进行详细、高效的人体成分分析。该研究纳入了 107 名在瑞典心肺生物影像研究（SCAPIS）中接受 3 层 CT 方案（涵盖肝脏、腹部和大腿）检查的受试者。该方案开发了用于自动评估肝脏衰减、内脏（VAT）和皮下（SAT）腹部脂肪组织、大腿肌肉、皮下、筋膜下（SFAT）和肌肉间脂肪组织的算法。使用手动参考测量对这些算法进行了验证。在能够肉眼识别阔筋膜（约 5%）的部分受试者中，对 SFAT 进行了研究。此外，还通过重复测量评估了手动测量内脏脂肪（IPAT）和腹膜后脂肪（RPAT）以及深、浅层 SAT 的精密度。自动化测量与手动参考测量高度相关。SFAT 储存区的相关性最弱（r=0.744）。自动 VAT 和 SAT 测量值略有但显著偏高（≤4.6%，p≤0.001）。腹部亚区的手动分割显示出很高的可重复性（CV≤8.1%，r≥0.930）。我们得出结论，低剂量 CT 扫描和自动分析使该方案适合大规模研究。
