Brain Tumor Segmentation using 3D UNET

Abstract

To enhance the efficiency of brain tumor diagnosis, we utilized UNet, a CNN architecture, for automatic MRI scan segmentation. Leveraging the BRATS 2018 dataset, which included a series of scans from patients with HGG and LGG. Our approach identifies tumor regions across various MRI sequences by full-volume scans into focused 3D slices. This method offers a faster, consistent alternative to manual segmentation, potentially improving outcomes through more rapid treatment.