Glioblastoma (GBM) is a highly aggressive brain tumor and is notoriously known for its intra-tumoral heterogeneity. Diagnosis of GBM is based on histopathology confirmation via tissue samples obtained from intra-cranial biopsies. After surgical intervention, histopathology tissue slides are visually analyzed by neuro-pathologists to identify distinct GBM histological hallmarks. GBMs may be histologically undergraded based on the amount of viable tissue due to sampling errors associated with small tissue samples obtained. Consequently, there is a need for automatic identification of histopathological GBM hallmarks. In this work, we present a hierarchical deep learning strategy to automatically segment distinct GBM niches including necrosis, cellular tumor, and hyperplastic blood-vessels, on H&E digitized histopathology slides. Our approach includes first segmenting necrosis and cellular tumor regions, then identifying hyperplastic blood-vessels within cellular tumor regions.