scholarly journals A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation

2019 ◽  
Vol 66 (4) ◽  
pp. 1069-1081 ◽  
Author(s):  
Ismail Irmakci ◽  
Sarfaraz Hussein ◽  
Aydogan Savran ◽  
Rita R. Kalyani ◽  
David Reiter ◽  
...  
Keyword(s):  
Body Composition ◽  
Body Tissue ◽  
Whole Body ◽  
Composition Analysis ◽  
Tissue Segmentation ◽  
Body Composition Analysis ◽  
Fuzzy Connectivity

scholarly journals Application of standards and models in body composition analysis

2015 ◽  
Vol 75 (2) ◽  
pp. 181-187 ◽  
Author(s):  
Manfred J. Müller ◽  
Wiebke Braun ◽  
Maryam Pourhassan ◽  
Corinna Geisler ◽  
Anja Bosy-Westphal
Keyword(s):  
Body Composition ◽  
Clinical Decision Making ◽  
Compartment Model ◽  
Clinical Decision ◽  
Whole Body ◽  
Composition Analysis ◽  
Body Composition Analysis ◽  
Physical Functions ◽  
Body Components

The aim of this review is to extend present concepts of body composition and to integrate it into physiology. In vivo body composition analysis (BCA) has a sound theoretical and methodological basis. Present methods used for BCA are reliable and valid. Individual data on body components, organs and tissues are included into different models, e.g. a 2-, 3-, 4- or multi-component model. Today the so-called 4-compartment model as well as whole body MRI (or computed tomography) scans are considered as gold standards of BCA. In practice the use of the appropriate method depends on the question of interest and the accuracy needed to address it. Body composition data are descriptive and used for normative analyses (e.g. generating normal values, centiles and cut offs). Advanced models of BCA go beyond description and normative approaches. The concept of functional body composition (FBC) takes into account the relationships between individual body components, organs and tissues and related metabolic and physical functions. FBC can be further extended to the model of healthy body composition (HBC) based on horizontal (i.e. structural) and vertical (e.g. metabolism and its neuroendocrine control) relationships between individual components as well as between component and body functions using mathematical modelling with a hierarchical multi-level multi-scale approach at the software level. HBC integrates into whole body systems of cardiovascular, respiratory, hepatic and renal functions. To conclude BCA is a prerequisite for detailed phenotyping of individuals providing a sound basis for in depth biomedical research and clinical decision making.

scholarly journals Stav antropometrických parametrů a tělesného složení u studentů Vojenského oboru UK FTVS v Praze

2015 ◽  
Vol 9 (2) ◽  
pp. 57-67 ◽  
Author(s):  
Ivana Kinkorová ◽  
Matěj Vrba
Keyword(s):  
Body Composition ◽  
Body Fat ◽  
The Body ◽  
Whole Body ◽  
Composition Analysis ◽  
Extracellular Water ◽  
Body Composition Analysis ◽  
Segmental Analysis ◽  
Total Body ◽  
General Physical

The aim of our study was the measurement of selected anthropometric variables, respectively determining somatotype, body composition analysis of students Military Department (MD) at UK FTVS in Prague and compared to similar studies. The group consisted of 22 probands, men ranging in age from 19–27 years (mean age = 22,9 ± 2,6 years, height = 179,9 ± 6,0 cm, weight = 76,8 ± 7,0 kg, BMI = 23,8 ± 1,5 kg.m–2). In terms of measured average somatotype (1,7 – 7,3 – 2,5), the students MD have very good preconditions for general physical fitness. We used BIA-Tanita MC 980 for the body composition analysis (whole body and segmental analysis). The students MD showed a high proportion of lean body mass (70,5 ± 6,1 kg) and low proportion of fat mass (8,3 ± 3,0 %). The authors emphasize the importance of monitoring and other parameters of body composition, e.g. total body water (TBW), extracellular water (ECW), intracellular water (ICW), segmental analysis of muscle mass and body fat.

scholarly journals Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

2020 ◽  
Author(s):  
Sven Koitka ◽  
Lennard Kroll ◽  
Eugen Malamutmann ◽  
Arzu Oezcelik ◽  
Felix Nensa
Keyword(s):  
Neural Networks ◽  
Body Composition ◽  
Correlation Coefficients ◽  
Ct Imaging ◽  
Body Tissue ◽  
Composition Analysis ◽  
Tissue Composition ◽  
Intra Class Correlation ◽  
Body Composition Analysis ◽  
Body Regions

Abstract Objectives Body tissue composition is a long-known biomarker with high diagnostic and prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible, and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. Methods Therefore, an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known Hounsfield unit limits. Results The Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. Conclusions Our results show that fully automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analyzing body composition in the clinical routine. Key Points • Our study enables fully automated body composition analysis on routine abdomen CT scans. • The best segmentation models for semantic body region segmentation achieved an averaged Sørensen Dice score of 0.9553. • Subclassified tissue volumes achieved intra-class correlation coefficients over 0.99.

scholarly journals Whole body composition analysis by the BodPod air-displacement plethysmography method in children with phenylketonuria shows a higher body fat percentage

2010 ◽  
Vol 33 (S3) ◽  
pp. 283-288 ◽  
Author(s):  
Monique Albersen ◽  
Marjolein Bonthuis ◽  
Nicole M. de Roos ◽  
Dorine A. M. van den Hurk ◽  
Ems Carbasius Weber ◽  
...  
Keyword(s):  
Body Composition ◽  
Body Fat ◽  
Whole Body ◽  
Composition Analysis ◽  
Body Fat Percentage ◽  
Fat Percentage ◽  
Air Displacement Plethysmography ◽  
Body Composition Analysis
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