Cargando…
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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1783671426575761408 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8002279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT ackermansleannelgc deeplearningautomatedsegmentationformuscleandadiposetissuefromabdominalcomputedtomographyinpolytraumapatients AT volmerleroy deeplearningautomatedsegmentationformuscleandadiposetissuefromabdominalcomputedtomographyinpolytraumapatients AT weeleonard deeplearningautomatedsegmentationformuscleandadiposetissuefromabdominalcomputedtomographyinpolytraumapatients AT brecheisenralph deeplearningautomatedsegmentationformuscleandadiposetissuefromabdominalcomputedtomographyinpolytraumapatients AT sanchezgonzalezpatricia deeplearningautomatedsegmentationformuscleandadiposetissuefromabdominalcomputedtomographyinpolytraumapatients AT seiffertalexanderp deeplearningautomatedsegmentationformuscleandadiposetissuefromabdominalcomputedtomographyinpolytraumapatients AT gomezenriquej deeplearningautomatedsegmentationformuscleandadiposetissuefromabdominalcomputedtomographyinpolytraumapatients AT dekkerandre deeplearningautomatedsegmentationformuscleandadiposetissuefromabdominalcomputedtomographyinpolytraumapatients AT tenboschjana deeplearningautomatedsegmentationformuscleandadiposetissuefromabdominalcomputedtomographyinpolytraumapatients AT oldedaminkstevenmw deeplearningautomatedsegmentationformuscleandadiposetissuefromabdominalcomputedtomographyinpolytraumapatients AT blokhuistacoj deeplearningautomatedsegmentationformuscleandadiposetissuefromabdominalcomputedtomographyinpolytraumapatients |