A Big Data Analytics Approach in Medical Imaging Segmentation Using Deep Convolutional Neural Networks

Abstract

Big data analytics uncovers hidden patterns, correlations and other insights by examining large amounts of data. Deep Learning can play a role in developing solutions from large datasets, and is an important tool in the big data analytics toolbox. Deep Learning has been recently employed to solve various problems in computer vision and demonstrated state-of-the-art performance on visual recognition tasks. In medical imaging, especially in brain tumor cancer diagnosis and treatment plan development, accurate and reliable brain tumor segmentation plays a critical role. In this chapter, we describe brain tumor segmentation using Deep Learning. We constructed a 6-layer Dense Convolutional Network, which connects each layer to every subsequent layer in a feed-forward fashion. This specific connectivity architecture ensures the maximum information flow between layers in the network and strengthens the feature propagation from layer to layer. We show how this arrangement increases the efficiency during training and the accuracy of the results. We have trained and evaluated our method based on the imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2017. The described method is able to segment the whole tumor (WT) region of the high-grade brain tumor gliomas using T1 Magnetic Resonance Images (MRI) and with excellent segmentation results.