3D Visualization and Depth Analysis of Lung Nodule from CT Images

Lung nodules are abnormal growths in the lungs that can be non-cancerous or cancerous. NSCLC (non-small cell lung cancer) is one of the most common types of lung cancer, which amounts to about 80% of all lung cancer cases. It easily spreads to other parts of the body if it is not treated early. Hence, early detection and treatment is essential. In recent years, three-dimensional (3D) visualization techniques have transformed the medical field, particularly in the diagnosis and treatment of lung nodules. 3D visualization involves generating a 3D model of lung nodules using imaging data from computed tomography (CT) scans. The resultant 3D model provides a more accurate and detailed representation of the nodule's shape, size, and location. This can aid in treatment planning and surgical navigation. In addition, depth analysis helps to measure the nodule's depth and distance from other structures in the lung, providing crucial information for treatment decisions. Initially the CT images are preprocessed and then the region of interest is identified. The lung tissue is segmented followed by nodule extraction. The extracted nodules are then visualized, and the depth analysis is performed. In this work, nodules are extracted using U net architecture and 3D visualization is performed through volume rendering.