Artificial intelligence-aided CT segmentation for body composition analysis : a validation study
(2021) In European Radiology Experimental 5(1).- 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... (More)
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 < 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%. Conclusions: The 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.
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- author
- Borrelli, Pablo ; Kaboteh, Reza ; Enqvist, Olof ; Ulén, Johannes LU ; Trägårdh, Elin LU ; Kjölhede, Henrik LU and Edenbrandt, Lars LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Body composition, computed), Muscles, Neural networks (computer), Subcutaneous fat, Tomography (x-ray
- in
- European Radiology Experimental
- volume
- 5
- issue
- 1
- article number
- 11
- publisher
- Springer
- external identifiers
-
- pmid:33694046
- scopus:85102358697
- ISSN
- 2509-9280
- DOI
- 10.1186/s41747-021-00210-8
- language
- English
- LU publication?
- yes
- id
- d92f3b81-03cc-40a7-9f32-872248f3d924
- date added to LUP
- 2021-03-23 07:53:30
- date last changed
- 2024-06-13 09:15:03
@article{d92f3b81-03cc-40a7-9f32-872248f3d924, abstract = {{<p>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 < 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%. Conclusions: The 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.</p>}}, author = {{Borrelli, Pablo and Kaboteh, Reza and Enqvist, Olof and Ulén, Johannes and Trägårdh, Elin and Kjölhede, Henrik and Edenbrandt, Lars}}, issn = {{2509-9280}}, keywords = {{Body composition; computed); Muscles; Neural networks (computer); Subcutaneous fat; Tomography (x-ray}}, language = {{eng}}, number = {{1}}, publisher = {{Springer}}, series = {{European Radiology Experimental}}, title = {{Artificial intelligence-aided CT segmentation for body composition analysis : a validation study}}, url = {{http://dx.doi.org/10.1186/s41747-021-00210-8}}, doi = {{10.1186/s41747-021-00210-8}}, volume = {{5}}, year = {{2021}}, }