We focused on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans.Tumor segmentation provides a milestone for determination of the exact tumor size for computer aided diagnosis (CAD). Liver lesion segmentation is a significant step in liver cancer diagnosis, treatment planning and treatment evaluation. Liver Tumor Segmentation Challenge (LiTS) offers a common testbed for comparing different automated liver lesion segmentation techniques. The main use of digital image processing is to increase the quality of images for interpretation of human and machine understanding. Tumor segmentation in liver computed tomography (CT) volumes is considered as a complex task because of the varying tumor shape and texture. The location of the tumor also presents a challenge. Manual segmentation of tumors is a time-consuming task that can be inaccurate in some cases. The aim of this research is to propose an automated method, which can detect the tumor in each slice in volumetric CT liver images.

Theme:

• Segmentation: Models and Optimization

• Cardiac MR Segmentation

• Brain Tumor Segmentation