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
Online Access:	View Fulltext in Publisher

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