An attention-based mechanism for brain tumor segmentation using four modalities
Brain tumor localization and extraction from magnetic resonance imaging (MRI) is a vital task in a wide variety of applications in the medical field. Current strategies demonstrate good performance on Non-Contrast-Enhanced T1-Weighted MRI, but this is not true when confronted with other modalities. Each modality represents different and vital information about the tissue we are working on. So, in this proposal, we propose an algorithm based on four modalities T1, T1c, T2, and FLAIR for segmenting the tumor region with a high rate of accuracy. To increase the efficiency of the model and decrease the evaluation time, a powerful pre-processing approach for removing the insignificant areas of the brain is used. Also, to improve the segmentation result of discrimination between internal areas of the tumor, an attention-based mechanism is used. We will use the BRATS 2018 dataset which comprises the Multi-Modal MRI images. Each patient’s sample in the dataset has the dimensions of 240×240×150 and were annotated by specialists.