Cargando…
Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation
The pectoralis muscle is an important indicator of respiratory muscle function and has been linked to various parenchymal biomarkers, such as airflow limitation severity and diffusing capacity for carbon monoxide, which are widely used in diagnosing parenchymal diseases, including asthma and chronic...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10479911/ https://www.ncbi.nlm.nih.gov/pubmed/37669295 http://dx.doi.org/10.1371/journal.pone.0290950 |
_version_ | 1785101693676945408 |
---|---|
author | Yang, Zepa Choi, Insung Choi, Juwhan Jung, Jongha Ryu, Minyeong Yong, Hwan Seok |
author_facet | Yang, Zepa Choi, Insung Choi, Juwhan Jung, Jongha Ryu, Minyeong Yong, Hwan Seok |
author_sort | Yang, Zepa |
collection | PubMed |
description | The pectoralis muscle is an important indicator of respiratory muscle function and has been linked to various parenchymal biomarkers, such as airflow limitation severity and diffusing capacity for carbon monoxide, which are widely used in diagnosing parenchymal diseases, including asthma and chronic obstructive pulmonary disease. Pectoralis muscle segmentation is a method for measuring muscle volume and mass for various applications. The segmentation method is based on deep-learning techniques that combine a muscle area detection model and a segmentation model. The training dataset for the detection model comprised multichannel images of patients, whereas the segmentation model was trained on 7,796 cases of the computed tomography (CT) image dataset of 1,841 patients. The dataset was expanded incrementally through an active learning process. The performance of the model was evaluated by comparing the segmentation results with manual annotations by radiologists and the volumetric differences between the CT image datasets of the same patients. The results indicated that the machine learning model is promising in segmenting the pectoralis major muscle, with good agreement between the automatic segmentation and manual annotations by radiologists. The training accuracy and loss values of the validation set were 0.9954 and 0.0725, respectively, and for segmentation, the loss value was 0.0579. This study shows the potential clinical usefulness of the machine learning model for pectoralis major muscle segmentation as a quantitative biomarker for various parenchymal and muscular diseases. |
format | Online Article Text |
id | pubmed-10479911 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104799112023-09-06 Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation Yang, Zepa Choi, Insung Choi, Juwhan Jung, Jongha Ryu, Minyeong Yong, Hwan Seok PLoS One Research Article The pectoralis muscle is an important indicator of respiratory muscle function and has been linked to various parenchymal biomarkers, such as airflow limitation severity and diffusing capacity for carbon monoxide, which are widely used in diagnosing parenchymal diseases, including asthma and chronic obstructive pulmonary disease. Pectoralis muscle segmentation is a method for measuring muscle volume and mass for various applications. The segmentation method is based on deep-learning techniques that combine a muscle area detection model and a segmentation model. The training dataset for the detection model comprised multichannel images of patients, whereas the segmentation model was trained on 7,796 cases of the computed tomography (CT) image dataset of 1,841 patients. The dataset was expanded incrementally through an active learning process. The performance of the model was evaluated by comparing the segmentation results with manual annotations by radiologists and the volumetric differences between the CT image datasets of the same patients. The results indicated that the machine learning model is promising in segmenting the pectoralis major muscle, with good agreement between the automatic segmentation and manual annotations by radiologists. The training accuracy and loss values of the validation set were 0.9954 and 0.0725, respectively, and for segmentation, the loss value was 0.0579. This study shows the potential clinical usefulness of the machine learning model for pectoralis major muscle segmentation as a quantitative biomarker for various parenchymal and muscular diseases. Public Library of Science 2023-09-05 /pmc/articles/PMC10479911/ /pubmed/37669295 http://dx.doi.org/10.1371/journal.pone.0290950 Text en © 2023 Yang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Yang, Zepa Choi, Insung Choi, Juwhan Jung, Jongha Ryu, Minyeong Yong, Hwan Seok Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation |
title | Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation |
title_full | Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation |
title_fullStr | Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation |
title_full_unstemmed | Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation |
title_short | Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation |
title_sort | deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10479911/ https://www.ncbi.nlm.nih.gov/pubmed/37669295 http://dx.doi.org/10.1371/journal.pone.0290950 |
work_keys_str_mv | AT yangzepa deeplearningbasedpectoralismusclevolumesegmentationmethodfromchestcomputedtomographyimageusingsagittalrangedetectionandaxialslicebasedsegmentation AT choiinsung deeplearningbasedpectoralismusclevolumesegmentationmethodfromchestcomputedtomographyimageusingsagittalrangedetectionandaxialslicebasedsegmentation AT choijuwhan deeplearningbasedpectoralismusclevolumesegmentationmethodfromchestcomputedtomographyimageusingsagittalrangedetectionandaxialslicebasedsegmentation AT jungjongha deeplearningbasedpectoralismusclevolumesegmentationmethodfromchestcomputedtomographyimageusingsagittalrangedetectionandaxialslicebasedsegmentation AT ryuminyeong deeplearningbasedpectoralismusclevolumesegmentationmethodfromchestcomputedtomographyimageusingsagittalrangedetectionandaxialslicebasedsegmentation AT yonghwanseok deeplearningbasedpectoralismusclevolumesegmentationmethodfromchestcomputedtomographyimageusingsagittalrangedetectionandaxialslicebasedsegmentation |