In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 9 ( 2023-9-5), p. e0290950-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0290950
DOI:
10.1371/journal.pone.0290950.g001
DOI:
10.1371/journal.pone.0290950.g002
DOI:
10.1371/journal.pone.0290950.g003
DOI:
10.1371/journal.pone.0290950.g004
DOI:
10.1371/journal.pone.0290950.g005
DOI:
10.1371/journal.pone.0290950.g006
DOI:
10.1371/journal.pone.0290950.g007
DOI:
10.1371/journal.pone.0290950.t001
DOI:
10.1371/journal.pone.0290950.t002
DOI:
10.1371/journal.pone.0290950.t003
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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