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It is a standalone application that can help radiologist in segmenting liver (DICOM image) using a region growing function and contouring to find the area of the segmented liver along with manual segmentation where the radiologist can segment the diseased liver manually along with providing notes for the segmented region.
Liver Tumor Detection using Multiclass Semantic Segmentation with U-Net Model Architecture. CT-Scan images processed with Window Leveling and Window Blending Method, also CT-Scan Mask processed with One Hot Semantic Segmentation (OHESS)
Liver cancer is one of the most dangerous diseases and is one of causes leading of death. The application of science and technology in the diagnosis and identification of cancerous tissues of the liver plays a very important role. This assists the doctor in planning and treating the patient. In this paper, we study the application of convolution…
Code for MICCAI 2016 paper : Automatic liver and lesions segmentation using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields