Deep Learning-Based Lung Nodule Segmentation and 3D Reconstruction Algorithm for CT Images

In recent years, deep learning techniques have been widely applied in the field of medical image processing, particularly in the segmentation of pulmonary nodules, which has garnered increasing attention from researchers. The segmentation of pulmonary computed tomography (CT) images is fundamental to the three-dimensional reconstruction of medical images, and the accuracy of image segmentation directly impacts the practical application value of three-dimensional reconstruction in the medical field. However, within the entire CT image of the lungs, pulmonary nodules only occupy a small region, and the complex nature of nodule manifestations and significant size variations present a major challenge for accurate segmentation in modern medicine. To address the issues of precise segmentation of pulmonary nodule CT images and the occurrence of voids in three-dimensional reconstruction, this study focuses on pulmonary CT images and achieves both accurate pulmonary nodule segmentation and three-dimensional reconstruction.