Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation

被引:2
作者
Yang, Zepa [1 ]
Choi, Insung [1 ]
Choi, Juwhan [2 ]
Jung, Jongha [1 ]
Ryu, Minyeong [1 ]
Yong, Hwan Seok [1 ]
机构
[1] Korea Univ Guro Hosp, Dept Radiol, Seoul, South Korea
[2] Korea Univ Guro Hosp, Dept Internal Med, Div Pulm Allergy & Crit Care Med, Seoul, South Korea
来源
PLOS ONE | 2023年 / 18卷 / 09期
基金
新加坡国家研究基金会;
关键词
BODY-MASS INDEX; OBSTRUCTIVE PULMONARY-DISEASE; CROSS-SECTIONAL AREA; SKELETAL-MUSCLE; ADIPOSE-TISSUE; COPD; SARCOPENIA; CT; MORTALITY; PREDICTOR;
D O I
10.1371/journal.pone.0290950
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:16
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共 46 条
  • [1] Abdulkadir A., 2016, INT C MED IM COMP CO, P424, DOI [DOI 10.1007/978, DOI 10.1007/978-3-319-46723-8_49]
  • [2] Updated systematic review and meta-analysis on diagnostic issues and the prognostic impact of myosteatosis: A new paradigm beyond sarcopenia
    Ahn, Hyemin
    Kim, Dong Wook
    Ko, Yousun
    Ha, Jiyeon
    Bin Shin, Young
    Lee, Jiwoo
    Sung, Yu Sub
    Kim, Kyung Won
    [J]. AGEING RESEARCH REVIEWS, 2021, 70
  • [3] Chronic obstructive pulmonary disease as a systemic disease: an epidemiological perspective
    Andreassen, H
    Vestbo, J
    [J]. EUROPEAN RESPIRATORY JOURNAL, 2003, 22 : 2S - 4S
  • [4] Computed tomography-derived area and density of pectoralis muscle associated disease severity and longitudinal changes in chronic obstructive pulmonary disease: a case control study
    Bak, So Hyeon
    Kwon, Sung Ok
    Han, Seon-Sook
    Kim, Woo Jin
    [J]. RESPIRATORY RESEARCH, 2019, 20 (01)
  • [5] Automatic MRI segmentation of pectoralis major muscle using deep learning
    Barros Godoy, Ivan Rodrigues
    Silva, Raian Portela
    Rodrigues, Tatiane Cantarelli
    Skaf, Abdalla Youssef
    Pochini, Alberto de Castro
    Yamada, Andre Fukunishi
    [J]. SCIENTIFIC REPORTS, 2022, 12 (01)
  • [6] Comparison of a New Integral-Based Half-Band Method for CT Measurement of Peripheral Airways in COPD With a Conventional Full-Width Half-Maximum Method Using Both Phantom and Clinical CT Images
    Cho, Young Hoon
    Seo, Joon Beom
    Kim, Namkug
    Lee, Hyun Joo
    Hwang, Hye Jeon
    Kim, Eun Young
    Oh, Sang Young
    [J]. JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2015, 39 (03) : 428 - 436
  • [7] Crisafulli Ernesto, 2007, Int J Chron Obstruct Pulmon Dis, V2, P19, DOI 10.2147/copd.2007.2.1.19
  • [8] Pectoralis muscle area and mortality in smokers without airflow obstruction
    Diaz, Alejandro A.
    Martinez, Carlos H.
    Harmouche, Rola
    Young, Thomas P.
    McDonald, Merry-Lynn
    Ross, James C.
    Han, Mei Lan
    Bowler, Russell
    Make, Barry
    Regan, Elizabeth A.
    Silverman, Edwin K.
    Crapo, James
    Boriek, Aladin M.
    Kinney, Gregory L.
    Hokanson, John E.
    Estepar, Raul San Jose
    Washko, George R.
    [J]. RESPIRATORY RESEARCH, 2018, 19
  • [9] Chest CT Measures of Muscle and Adipose Tissue in COPD: Gender-based Differences in Content and in Relationships with Blood Biomarkers
    Diaz, Alejandro A.
    Zhou, Linfu
    Young, Tom P.
    McDonald, Merry-Lynn
    Harmouche, Rola
    Ross, James C.
    Estepar, Raul San Jose
    Wouters, Emiel F. M.
    Coxson, Harvey O.
    MacNee, William
    Rennard, Stephen
    Maltais, Francois
    Kinney, Gregory L.
    Hokanson, John E.
    Washko, George R.
    [J]. ACADEMIC RADIOLOGY, 2014, 21 (10) : 1255 - 1261
  • [10] CT-Based Segmentation of Pectoral Muscle using Deep Learning and Association of Computed Metrics with Aging and Sex
    Dutta, Indra Narayan
    Nadeem, Syed Ahmed
    Comellas, Alejandro P.
    Hoffman, Eric A.
    Saha, Punam K.
    [J]. MEDICAL IMAGING 2022: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING, 2022, 12036