Deep learning-based respiratory muscle segmentation as a potential imaging biomarker for respiratory function assessment
Insung Choi, Juwhan Choi, Hwan Seok Yong, Zepa Yang
Abstract
Respiratory diseases significantly affect respiratory function, making them a considerable contributor to global mortality. The respiratory muscles play an important role in disease prognosis; as such, quantitative analysis of the respiratory muscles is crucial to assess the status of the respiratory system and the quality of life in patients.
Introduction
The lungs are essential organs responsible for respiration to sustain life. Diseases that impair lung function pose a significant threat to patient health. Specifically, conditions such as Chronic Obstructive Pulmonary Disease (COPD) and asthma have serious implications for a patient’s quality of life (QoL).
Material and Methods
This retrospective study was conducted in compliance with the Helsinki Declaration and was approved by the Institutional Review Board (IRB) of Korea University Guro Hospital (IRB No.: 2022GR0185, 2018GR0179, and 2021GR0335).
Result
The segmentation results for the respiratory muscles obtained using the proposed methodology are shown in Figs 4 and 5.
Discussion
In this study, we developed a deep-learning-based respiratory muscle segmentation and classification model capable of quantifying the volume and density of respiratory muscles from CT images.
Conclusion
In this study, we developed a deep-learning-based model for the segmentation and classification of respiratory muscles, and further verified its capability to measure the volume and density of respiratory muscles precisely in CT images.
Citation: Choi I, Choi J, Yong HS, Yang Z (2024) Deep learning-based respiratory muscle segmentation as a potential imaging biomarker for respiratory function assessment. PLoS ONE 19(7): e0306789. https://doi.org/10.1371/journal.pone.0306789
Editor: Minsoo Chun, Chung-Ang University Gwangmyeong Hospital, REPUBLIC OF KOREA
Received: January 31, 2024; Accepted: June 24, 2024; Published: July 26, 2024
Copyright: © 2024 Choi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Muscle tissue segmentation model (IRB No.: 2022GR0185): This model was exclusively trained using the open dataset from "The Open AI Dataset Project (AI-Hub, S. Korea)" which is publicly accessible via 'AI-Hub [http://www.aihub.or.kr]. Respiratory muscle segmentation and classification model (IRB No.: 2022GR0185), Additional experimental analysis (IRB No.: 2018GR0179 and 2021GR0335): Unlike the open AI-Hub dataset used in the first model, the data employed for this model and its subsequent analysis are sourced from the Korea University Medical Center. Due to IRB and DRB data share restrictions, this data cannot be publicly shared and is only available to researchers who meet specific access criteria, as regulated by the Korea University Medical Center Institutional Review Board and Data Access / Ethics Committee [https://irb.kumc.or.kr or jihyeee@korea.ac.kr].
Funding: This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR20C0021).
Competing interests: The authors have declared that no competing interests exist.
Source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0306789#abstract0
