Fully-Automatic Segmentation of Gliomas using MR Images

The most prevailing and most deadly form of the brain tumor is glioma, with a mortality rate of around 70%. Therefore, early and efficient treatment is necessary for improving patients' wellbeing. The MRI method has become a popular method for identifying these tumors, but the amount of data retrieved limits the use of quantitative measures that are accurate and reproducible. Therefore, Automating brain tumor segmentation from MR images is essential for tumor analysis and monitoring. For successful tumor delineation, accurate and reliable tumor segmentation techniques are used for gliomas, which are aggressive and heterogeneous. With the use of CNNs, which are widely utilized for biomedical image segmentation, the accuracy of the current state-of-the-art has greatly improved on the problem of segmenting brain tumors. This paper proposes a M U-Net architecture with a PReLU activation layer and smaller input kernels to increase the performance of segmenting tumor regions in MR images. An augmentation of data combined with normalization during preprocessing is applied to rectify the issue of class-imbalance in tumor segmentation. This experiment uses the BRATS 2017 dataset. It achieved accuracy, sensitivity, specificity of 97.94%, 99.9%, and 92.58% respectively.