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

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.

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 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.

Osteopenia assessed by body composition analysis is related to malnutrition in alcoholic patients

Alcohol ◽  
2000 ◽  
Vol 22 (3) ◽  
pp. 147-157 ◽  
Author(s):  
Francisco Santolaria ◽  
Emilio González-Reimers ◽  
José Luis Pérez-Manzano ◽  
Antonio Milena ◽  
Marı́a Angeles Gómez-Rodrı́guez ◽  
...  
Keyword(s):  
Body Composition ◽  
Composition Analysis ◽  
Body Composition Analysis
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