Concordance of Computed Tomography Regional Body Composition Analysis Using a Fully Automated Open-Source Neural Network versus a Reference Semi-Automated Program with Manual Correction

Concordance of Computed Tomography Regional Body Composition Analysis Using a Fully Automated Open-Source Neural Network versus a Reference Semi-Automated Program with Manual Correction

Quick, efficient, fully automated open-source programs to segment muscle and adipose tissues from computed tomography (CT) images would be a great contribution to body composition research. This study examined the concordance of cross-sectional areas (CSA) and densities for muscle, visceral adipose...

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Bibliographic Details
Main Authors: Byrne, C. (Author), Geesey, T. (Author), Gomez-Perez, S.L (Author), Peterson, S. (Author), Sclamberg, J. (Author), Wakefield, C. (Author), Zhang, Y. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
adipose tissue
Adipose tissue
agreement
artificial intelligence
automated segmentation
Automated segmentation
Automation
Biochemistry
body composition
Body composition
computed tomography
Computed tomography images
Computerized tomography
Diagnosis
Diseases
Image segmentation
Intramuscular
muscle
Muscle
Neural-networks
Similarity coefficients
Software testing
Subcutaneous adipose tissues
Test method
validation
Validation
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