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Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Med, BioVenture Hub, Molndal, Sweden.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Med, BioVenture Hub, Molndal, Sweden.
Sahlgrens Univ Hosp, Dept Radiol, Gothenburg, Sweden.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.ORCID iD: 0000-0001-7764-1787
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, 10425Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 7, 10425
National Category
Medical and Health Sciences
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URN: urn:nbn:se:uu:diva-329273DOI: 10.1038/s41598-017-08925-8ISI: 000409309300013PubMedID: 28874743OAI: oai:DiVA.org:uu-329273DiVA: diva2:1140267
Funder
Swedish Research Council, 2012-2330Swedish Heart Lung FoundationVINNOVA
Available from: 2017-09-11 Created: 2017-09-11 Last updated: 2017-12-06Bibliographically approved

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Kullberg, JoelHedström, AndersStrand, RobinJohansson, Lars EAhlström, Håkan

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