Three-dimensional image reconstruction based on improved U-net network for anatomy of pulmonary segmentectomy

Abstract

Pulmonary segmentectomy is one of the advanced techniques in thoracic surgery, but it is difficult to understand and master because of its complex anatomical structure. The purpose of this study is to explore the application effect of three-dimensional (3D) image reconstruction based on an improved U-net network in the anatomy of thoracic surgery. In this study, a total of 40 standardization training residents of thoracic surgery in our hospital were randomly divided into two groups. The control group was taught by conventional thin-slice CT images, while the observation group was taught by 3D image reconstruction based on the improved U-net network. After the training process was completed, the teaching effect was compared between these two groups. Using the improved U-net network model, 3D reconstruction of pulmonary segments can be realized quickly. Compared with the control group, the individual and total objective scores in the observation group were higher. The satisfaction of learning interest, content understanding, clinical thinking mode, and understanding of operation process in the observation group was higher than that of the control group. From the results, we concluded that the 3D image reconstruction technology based on the improved U-net network could help students master the anatomical structure of pulmonary segments and improve their learning interest and clinical thinking ability.