PDF(Pubmed)
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. In this study, we aimed to develop an automated approach for the segmentation and classification of three types of respiratory muscles from computed tomography (CT) images using artificial intelligence. With a dataset of approximately 600,000 thoracic CT images from 3,200 individuals, we trained the model using the Attention U-Net architecture, optimized for detailed and focused segmentation. Subsequently, we calculated the volumes and densities from the muscle masks segmented by our model and performed correlation analysis with pulmonary function test (PFT) parameters. The segmentation models for muscle tissue and respiratory muscles obtained dice scores of 0.9823 and 0.9688, respectively. The classification model, achieving a generalized dice score of 0.9900, also demonstrated high accuracy in classifying thoracic region muscle types, as evidenced by its F1 scores: 0.9793 for the pectoralis muscle, 0.9975 for the erector spinae muscle, and 0.9839 for the intercostal muscle. In the correlation analysis, the volume of the respiratory muscles showed a strong correlation with PFT parameters, suggesting that respiratory muscle volume may serve as a potential novel biomarker for respiratory function. Although muscle density showed a weaker correlation with the PFT parameters, it has a potential significance in medical research.
摘要：
呼吸系统疾病显著影响呼吸功能，使它们成为全球死亡率的重要贡献者。呼吸肌在疾病预后中起着重要作用；因此，呼吸肌肉的定量分析对于评估患者的呼吸系统状况和生活质量至关重要。在这项研究中,我们的目的是开发一种自动化方法，用于使用人工智能从计算机断层扫描（CT）图像中分割和分类三种类型的呼吸肌。拥有来自3200个人的大约600,000张胸部CT图像的数据集，我们使用注意力U-Net架构训练模型，针对详细和集中的分割进行了优化。随后，我们计算了模型分割的肌肉面罩的体积和密度,并与肺功能测试(PFT)参数进行了相关性分析.肌肉组织和呼吸肌的分割模型分别获得0.9823和0.9688的骰子得分。分类模型，达到0.9900的广义骰子得分，也证明了在分类胸区肌肉类型的高准确性，其F1评分证明：胸肌为0.9793，竖脊肌0.9975，肋间肌为0.9839。在相关性分析中，呼吸肌的体积与PFT参数有很强的相关性，提示呼吸肌容量可能是呼吸功能的潜在新生物标志物。尽管肌肉密度与PFT参数的相关性较弱，它在医学研究中具有潜在的意义。
