Combination of Graph and Convolutional Networks for Brain Tumor Segmentation from Multi-Modal MR Images In Clinical Applications - Marjan Vatanpour; Javad Haddadnia; Shahryar Salmani Bajestani (Frontiers in Biomedical Technologies)
Purpose: Brain tumors are very important for the overall health of humans, which happen due to the uncontrolled increase and duplication of abnormal cells. Therefore, brain tumor segmentation is a very important step in medical diagnosis and can help in early tumor detection, treatment planning, and tumor progression follow-ups. To solve the problems related to manual segmentation such as time-cost, inaccuracy and subjectivity, automatic segmentation with deep learning methods is presented. This study aimed to develop an automatic brain tumor segmentation based on the combination of convolutional and graph neural networks to overcome the shortcomings of each network when they are used individually.