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

• Published in: PloS one Vol. 18; no. 9; p. e0290950
• Main Authors: Yang, Zepa, Choi, Insung, Choi, Juwhan, Jung, Jongha, Ryu, Minyeong, Yong, Hwan Seok
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 05.09.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Air flow; Analysis; Annotations; Asthma; Automation; Biology and Life Sciences; Biomarkers; Body composition; Carbon monoxide; Chronic obstructive pulmonary disease; Computed tomography; Computer and Information Sciences; CT imaging; Datasets; Deep learning; Evaluation; Image processing; Image segmentation; Learning algorithms; Lung diseases; Lung diseases, Obstructive; Machine learning; Measurement methods; Medical diagnosis; Medical imaging; Medicine and Health Sciences; Methods; Modelling; Muscles; Muscular diseases; Obstructive lung disease; Pectoralis muscles; People and Places; Research and Analysis Methods; Sarcopenia; Tomography; Training; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0290950

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.