Gliomas are the most frequent primary brain tumors in adults. Improved quantification of the various aspects of a glioma requires accurate segmentation of the tumor in magnetic resonance images (MRI). Since the manual segmentation is time-consuming and subject to human error and irre-producibility, automatic segmentation has received a lot of attention in recent years. This paper presents a fully automated segmentation method which is capable of automatic segmentation of brain tumor from multi-modal MRI scans. The proposed method is comprised of a deeply-supervised neural network based on Holistically-Nested Edge Detection (HED) network. The HED method, which is originally developed for the binary classification task of image edge detection, is extended for multiple-class segmentation. The classes of interest include the whole tumor, tumor core, and enhancing tumor. The dataset provided by 2017 Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) challenge is used in this work for training the neural network and performance evaluations. Experiments on BraTS 2017 challenge datasets demonstrate that the method performs well compared to the existing works. The assessments revealed the Dice scores of 0.86, 0.60, and 0.69 for whole tumor, tumor core, and enhancing tumor classes, respectively.
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