Artificial intelligence-aided CT segmentation for body composition analysis: a validation study

European Radiology Experimental

Elin Trägårdh¹  Johannes Ulén²  Olof Enqvist³  Reza Kaboteh⁴  Pablo Borrelli⁴  Lars Edenbrandt⁵  Henrik Kjölhede⁶ 

[1] Department of Clinical Physiology and Nuclear Medicine, Lund University and Skåne University Hospital, Malmö, Sweden;Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden;Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden;Eigenvision AB, Malmö, Sweden;Region Västra Götaland, Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden;Region Västra Götaland, Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden;Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden;Region Västra Götaland, Department of Urology, Sahlgrenska University Hospital, Gothenburg, Sweden;Department of Urology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden;

关键词: Body composition;    Muscles;    Neural networks (computer);    Subcutaneous fat;    Tomography (x-ray;    computed);   

DOI  :  10.1186/s41747-021-00210-8

来源: Springer

【 摘 要 】

BackgroundBody composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.MethodsEthical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3 days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations.ResultsThe accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p < 0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of ± 20%.ConclusionsThe AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.

【 授权许可】

CC BY