In this project, we took the raw MRI images from the REMBRANDT TCIA collection and processed them through a well-known image processing segmentation pipeline specialized for brain cancer MRI images. The raw images in DICOM file format were first cleaned to include only MRI scans from 4 modalities (T1, T1-C, T2, FLAIR) . After cleaning and pre-processing, automated volumetric segmentation was performed using tool GLISTRboost. This identified various subregions of the brain including necrotic core, edema, NET and ET, GM, WM, and Cerebrospinal Fluid (CSF) (not all segments were present for every patient). This dataset will now allow researchers to perform radiogenomics based analysis, integrate with gene expression and copy number data, and enable new discoveries and hypotheses.