scholarly journals Methodology, clinical applications, and future directions of body composition analysis of computed tomography (CT) images: A review

2021 ◽  
pp. 109943
Author(s):  
Antti Tolonen ◽  
Tomppa Pakarinen ◽  
Antti Sassi ◽  
Jere Kyttä ◽  
William Cancino ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Body Composition ◽  
Ct Images ◽  
Clinical Applications ◽  
Composition Analysis ◽  
Future Directions ◽  
Body Composition Analysis

scholarly journals Muscle segmentation in axial computed tomography (CT) images at the lumbar (L3) and thoracic (T4) levels for body composition analysis

2019 ◽  
Vol 75 ◽  
pp. 47-55 ◽  
Author(s):  
Setareh Dabiri ◽  
Karteek Popuri ◽  
Elizabeth M. Cespedes Feliciano ◽  
Bette J. Caan ◽  
Vickie E. Baracos ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Body Composition ◽  
Ct Images ◽  
Composition Analysis ◽  
Body Composition Analysis ◽  
Muscle Segmentation

scholarly journals Technical Note: Automatic segmentation of CT images for ventral body composition analysis

2020 ◽  
Vol 47 (11) ◽  
pp. 5723-5730
Author(s):  
Yabo Fu ◽  
Joseph E. Ippolito ◽  
Daniel R. Ludwig ◽  
Rehan Nizamuddin ◽  
Harold H. Li ◽  
...  
Keyword(s):  
Body Composition ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Technical Note ◽  
Composition Analysis ◽  
Body Composition Analysis

Post-mortem limitations of body composition analysis by computed tomography

Ergonomics ◽  
1994 ◽  
Vol 37 (1) ◽  
pp. 207-216 ◽  
Author(s):  
V. JANSSENS ◽  
P. THYS ◽  
J. P. CLARYS ◽  
H. Kvis ◽  
B. CHOWDHURY ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Body Composition ◽  
Composition Analysis ◽  
Post Mortem ◽  
Body Composition Analysis

Reliability and validity of the new VikingSlice software for computed tomography body composition analysis

2018 ◽  
Vol 73 (1) ◽  
pp. 54-61 ◽  
Author(s):  
Imanta Ozola-Zālīte ◽  
Esben Bolvig Mark ◽  
Tomas Gudauskas ◽  
Vladimir Lyadov ◽  
Søren Schou Olesen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Body Composition ◽  
Reliability And Validity ◽  
Composition Analysis ◽  
Body Composition Analysis

Computed Tomography–based Body Composition Analysis and Its Role in Lung Cancer Care

2020 ◽  
Vol 35 (2) ◽  
pp. 91-100 ◽  
Author(s):  
Amelie S. Troschel ◽  
Fabian M. Troschel ◽  
Till D. Best ◽  
Henning A. Gaissert ◽  
Martin Torriani ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Body Composition ◽  
Cancer Care ◽  
Composition Analysis ◽  
Body Composition Analysis

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

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Pablo Borrelli ◽  
Reza Kaboteh ◽  
Olof Enqvist ◽  
Johannes Ulén ◽  
Elin Trägårdh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Body Composition ◽  
Three Dimensional ◽  
Test Group ◽  
Training Group ◽  
Ct Images ◽  
Composition Analysis ◽  
Individual Subject ◽  
Body Composition Analysis ◽  
Ct Segmentation

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 < 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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