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Research Article
Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment
Soon Ho Yoon
Seoul National University Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3700-0165
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This work is licensed under a CC BY 4.0 License. Read Full License
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- This study involved human subjects.
- The author confirmed that all appropriate ethical guidelines for the use of human subjects have been followed, any necessary IRB and/or ethics committee review has been obtained, and information about the IRB/ethics committee is included in the manuscript.
- The author has confirmed that all necessary patient/participant consent or assent has been obtained and the appropriate institutional forms have been archived. If the IRB/ethics committee waived the requirement for patient/participant consent or assent, an explanation for the waiver is included in the text.
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural network applicable to whole-body CT images of PET-CT scan for the automatic volumetric segmentation of body composition.
Methods For model development, CT images of 100 patients who underwent a whole-body or torso 18F-fluorodeoxyglucose PET–CT scan were retrospectively included. Two radiologists semi-automatically labeled the following seven body components in every CT image slice, providing 39,268 images for training the 3D U-Net: skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs with vessels, and central nervous system. The segmentation accuracy was assessed using reference masks from one internal and three external datasets: two domestic centers (n=20, each) and a French public dataset (n=24). The 3D U-Net-driven values were clinically validated using bioelectrical impedance analysis (BIA) and by assessing the model’s diagnostic performance for sarcopenia in a community-based elderly cohort (n=522).
Results The 3D U-Net achieved accurate body composition segmentation with an average dice similarity coefficient of 96.5% to 98.9% for all masks and 92.3% to 99.3% for muscle, abdominal visceral fat, and subcutaneous fat in the validation datasets. The 3D U-Net-derived torso volume of skeletal muscle and fat tissue and the average area of those tissues in the waist were correlated with BIA-derived appendicular lean mass (correlation coefficients: 0.71 and 0.72, each) and fat mass (correlation coefficients: 0.95 and 0.93, each). The 3D U-Net-derived average areas of skeletal muscle and fat tissue in the waist were independently associated with sarcopenia (P<.001, each) with adjustment for age and sex, providing an area under the curve of 0.858 (95% CI, 0.815 to 0.901).
Conclusions This deep neural network model enabled the automatic volumetric segmentation of body composition on whole-body CT images, potentially expanding adjunctive sarcopenia assessment on PET-CT scan and volumetric assessment of metabolism in whole-body muscle and fat tissues.
Keywords
sarcopenia, computed tomography, deep learning, segmentation, body composition
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Research Article
Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment
Soon Ho Yoon
Seoul National University Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3700-0165
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 16 Dec, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Nuclear Medicine & Medical Imaging
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- This study involved human subjects.
- The author confirmed that all appropriate ethical guidelines for the use of human subjects have been followed, any necessary IRB and/or ethics committee review has been obtained, and information about the IRB/ethics committee is included in the manuscript.
- The author has confirmed that all necessary patient/participant consent or assent has been obtained and the appropriate institutional forms have been archived. If the IRB/ethics committee waived the requirement for patient/participant consent or assent, an explanation for the waiver is included in the text.
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural network applicable to whole-body CT images of PET-CT scan for the automatic volumetric segmentation of body composition.
Methods For model development, CT images of 100 patients who underwent a whole-body or torso 18F-fluorodeoxyglucose PET–CT scan were retrospectively included. Two radiologists semi-automatically labeled the following seven body components in every CT image slice, providing 39,268 images for training the 3D U-Net: skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs with vessels, and central nervous system. The segmentation accuracy was assessed using reference masks from one internal and three external datasets: two domestic centers (n=20, each) and a French public dataset (n=24). The 3D U-Net-driven values were clinically validated using bioelectrical impedance analysis (BIA) and by assessing the model’s diagnostic performance for sarcopenia in a community-based elderly cohort (n=522).
Results The 3D U-Net achieved accurate body composition segmentation with an average dice similarity coefficient of 96.5% to 98.9% for all masks and 92.3% to 99.3% for muscle, abdominal visceral fat, and subcutaneous fat in the validation datasets. The 3D U-Net-derived torso volume of skeletal muscle and fat tissue and the average area of those tissues in the waist were correlated with BIA-derived appendicular lean mass (correlation coefficients: 0.71 and 0.72, each) and fat mass (correlation coefficients: 0.95 and 0.93, each). The 3D U-Net-derived average areas of skeletal muscle and fat tissue in the waist were independently associated with sarcopenia (P<.001, each) with adjustment for age and sex, providing an area under the curve of 0.858 (95% CI, 0.815 to 0.901).
Conclusions This deep neural network model enabled the automatic volumetric segmentation of body composition on whole-body CT images, potentially expanding adjunctive sarcopenia assessment on PET-CT scan and volumetric assessment of metabolism in whole-body muscle and fat tissues.
Figure 1
Figure 2
Figure 3
Figure 4
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.