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Gliomas are very heterogenous set of tumors that grow within the substance of brain and often mix with normal brain tissues. Due to its histologic complexity and irregular shapes, multiparametric magnetic resonance imaging is used to accurately diagnose brain tumor and their subregions. Current practice requires physicians to manually segment these regions on a large image dataset, which can be a very time consuming and complicated task especially with large variations among different tumor regions. Automatic segmentation of brain tumors in multimodal MRI holds a great potential in developing an effective treatment plan and improving brain tumor radiotherapy workflow. Despite continuous investigations on DL-based brain tumor segmentation, irregular shapes and histologic complexities of brain tumors introduces a major challenge in developing an effective automatic segmentation method. In this study, we develop a novel context U-net with deep supervision to segment both the whole brain tumor and their subregions of tumors. The context module was formed by an inception-like structure to extract more information regarding the brain tumors. The deep supervision in the encoder path was achieved by adding up the segmentation outputs at different levels of the network. We evaluated our method on Brain Tumor Segmentation Challenge (BraTS) 2019 training dataset in which 80% was used for training while the remaining 20% was used for performance testing. Our method achieved Dice similarity coefficients (DSC) were 0.8693, 0.8013 and 0.7782 for the whole tumor (WT), tumor core (TC) and enhancing tumor (ET), respectively. The results attained by our proposed network suggested that this technique could be used for segmentation of brain tumors and their subregions to facilitate the brain tumor radiotherapy workflow.
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