3D Hyper-Dense Connected Convolutional Neural Network for Brain Tumor Segmentation

Glioma is one of the most widespread and intense forms of primary brain tumors. Accurate subcortical brain segmentation is essential in the evaluation of gliomas which helps to monitor the growth of gliomas and assists in the assessment of medication effects. Manual segmentation is needed a lot of human resources on Magnetic Resonance Imaging (MRI) data. Deep learning methods have become a powerful tool to learn features automatically in medical imaging applications including brain tissue segmentation, liver segmentation, and brain tumor segmentation. The shape of gliomas, structure, and location are different among individual patients, and it is a challenge to developing a model. In this paper, 3D hyper-dense Convolutional Neural Network(Cnn)is developed to segment tumors, in which it captures the global and local contextual information from two scales of global and local patches along with the two scales of receptive field. Densely connected blocks are used to exploit the benefit of a CNN to boost the model segmentation performance in Enhancing Tumor (ET), Non-Enhancing Tumor (NET), and Peritumoral Edema (PE). This dense architecture adopts 3D Fully Convolutional Network (FCN) architecture that is used for end-to-end volumetric prediction. The dense connectivity can offer a chance of deep supervision and improve gradient flow information in the learning process. The network is trained hierarchically based on global and local patches. In this scenario, the both patches are processed in their separate path, and dense connections happen not only between same path layers but also between different path layers. Our approach is validated on the BraTS 2018 dataset with the dice-score of 0.87, 0.81 and 0.84 for the complete tumor, enhancing tumor, and tumor core respectively. These outcomes are very close to the reported state-of-the-art results, and our approach is preferable to present 3D-based approaches when it comes to compactness, time and parameter efficiency on MRI brain tumor segmentation.