A 3D Processing Technique to Detect Lung Tumor

In this paper, the authors introduce a new segmentation technique based on U-NET algorithm from the deep learning used for lung cancer segmentation, which is the main challenge that medical Staff confront in their diagnosis process. The goal is to develop an ideal segmentation that enables medical personnel to distinguish the various tumor components using the completely U-NET convolution network architecture, which is the most effective. First, the regions of interest (ROI) in the 2D slides are established by an expert using the syngovia application of the Siemens. In this pre-processing step, the cancer area is isolated from its surroundings, and is used as a training model for U-NET algorithm. Second, the 2D U-NET model is used to segment the DICOM images (Digital Imaging and Communications in Medicine) into homogeneous regions. Finally, the post processing step has been used to obtain the 3D CT scan (computerized tomography) from the 2D slices. The segmentation results from the proposed method applied on biomedical images from nuclear medicine and radiotherapy that are extracted from the archiving system of the Institute of Salah Azaiez from Tunisia. The segmentation results are validated, and the prediction accuracy for the available test data is evaluated. Finally, a comparison study with other existing techniques is presented. The experimental results demonstrate the superiority of the used U-NET architecture applied either for 2D or for 3D image segmentation.