Research on Pectoral Muscle Segmentation Algorithm of CT Image Based on Deep Learning

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease, which seriously endangers human health and is also one of the important causes of death. The death rate of COPD in China is the highest in the world, and the problem of under-diagnosis of the disease is very serious. The gold standard for the diagnosis of COPD is lung function examination, and clinical studies have shown that CT and other imaging methods can be included in the auxiliary diagnosis of COPD. CT images can be used to assess pectoral muscle area, which is associated with COPD severity. Patients with lower pectoral muscle area often have more severe expiratory airflow obstruction and other problems. Therefore, the key of the research is to accurately segment the pectoral muscle in CT images. The medical image segmentation method based on deep learning can dig out more abundant internal information of data, so it has gradually become the preferred method in the aspect of medical image segmentation. In this paper, a pectoral muscle segmentation algorithm based on U-Net and its variant U-Net++ is proposed, which is of great significance for evaluating the severity of disease in patients with COPD. The network is composed of symmetrical encoders and decoders, which can effectively learn from very little labelled data by using appropriate data enhancement methods, and is therefore very suitable for medical image segmentation. The experimental results on the data set provided by Jiangsu Province Hospital show that the average Dice coefficient of the proposed algorithm is more than 94% and the average accuracy rate is 91%. The algorithm can accurately segment the pectoral muscle in CT images and has good segmentation performance.