Brain tumor segmentation using a multi-path CNN based method

Abstract

In this paper an automatic brain tumor segmentation approach based on a multi-path Convolutional Neural Network (CNN) is presented. Proposition of the method was motivated by the success of multi-path CNNs, DeepMedic[1] and the method presented in [2], where the local and contextual pieces of information for segmentation were obtained from multi-scale regions. In addition to that, the method exploits the fact that very often tumor introduces high asymmetry to the brain. In order to help model in distinguishing between brain lesions and healthy brain structures such as sulci, gyri and ventricles, the model is provided with spatial information, as well. The model’s training and hyper-parameter tuning were performed on the BraTS 2017 training dataset, model’s validation was done on the BraTS 2017 validation dataset and the final results are reported on the BraTS 2017 testing dataset. The average Dice scores obtained on the testing dataset are 0.6049, 0.8436 and 0.6938 for enhancing tumor, whole tumor and tumor core, respectively.