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

Autor: Pablo Borrelli, Reza Kaboteh, Olof Enqvist, Johannes Ulén, Elin Trägårdh, Henrik Kjölhede, Lars Edenbrandt
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: European Radiology Experimental, Vol 5, Iss 1, Pp 1-6 (2021)
Druh dokumentu: article
ISSN: 2509-9280
48538108
DOI: 10.1186/s41747-021-00210-8
Popis: Abstract Background Body 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. Methods Ethical 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. Results The 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
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