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Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery su...

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Autores principales: Ackermans, Leanne L. G. C., Volmer, Leroy, Wee, Leonard, Brecheisen, Ralph, Sánchez-González, Patricia, Seiffert, Alexander P., Gómez, Enrique J., Dekker, Andre, Ten Bosch, Jan A., Olde Damink, Steven M. W., Blokhuis, Taco J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8002279/
https://www.ncbi.nlm.nih.gov/pubmed/33809710
http://dx.doi.org/10.3390/s21062083
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author Ackermans, Leanne L. G. C.
Volmer, Leroy
Wee, Leonard
Brecheisen, Ralph
Sánchez-González, Patricia
Seiffert, Alexander P.
Gómez, Enrique J.
Dekker, Andre
Ten Bosch, Jan A.
Olde Damink, Steven M. W.
Blokhuis, Taco J.
author_facet Ackermans, Leanne L. G. C.
Volmer, Leroy
Wee, Leonard
Brecheisen, Ralph
Sánchez-González, Patricia
Seiffert, Alexander P.
Gómez, Enrique J.
Dekker, Andre
Ten Bosch, Jan A.
Olde Damink, Steven M. W.
Blokhuis, Taco J.
author_sort Ackermans, Leanne L. G. C.
collection PubMed
description Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.
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spelling pubmed-80022792021-03-28 Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients Ackermans, Leanne L. G. C. Volmer, Leroy Wee, Leonard Brecheisen, Ralph Sánchez-González, Patricia Seiffert, Alexander P. Gómez, Enrique J. Dekker, Andre Ten Bosch, Jan A. Olde Damink, Steven M. W. Blokhuis, Taco J. Sensors (Basel) Article Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers. MDPI 2021-03-16 /pmc/articles/PMC8002279/ /pubmed/33809710 http://dx.doi.org/10.3390/s21062083 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ackermans, Leanne L. G. C.
Volmer, Leroy
Wee, Leonard
Brecheisen, Ralph
Sánchez-González, Patricia
Seiffert, Alexander P.
Gómez, Enrique J.
Dekker, Andre
Ten Bosch, Jan A.
Olde Damink, Steven M. W.
Blokhuis, Taco J.
Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients
title Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients
title_full Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients
title_fullStr Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients
title_full_unstemmed Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients
title_short Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients
title_sort deep learning automated segmentation for muscle and adipose tissue from abdominal computed tomography in polytrauma patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8002279/
https://www.ncbi.nlm.nih.gov/pubmed/33809710
http://dx.doi.org/10.3390/s21062083
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