Automatic Brain Tumor Segmentation Method Based on Modified Convolutional Neural Network

In the domain of brain diseases, it is difficult for image registration after some brain structures are severely deformed because of diseases. Fortunately, convolutional neural network have gained many promising results in semantic segmentation challenging tasks in recent years. To enhance the performance of automatic brain tumor segmentation, this paper presents a robust segmentation algorithm based on convolutional neural network, which achieved improvement of 3.84% in segmenting the enhancing tumor. Our network architecture is developed from the prevalent U-Net. We combined it with ResNet and modified it to maximize its performance in our brain tumor segmentation task. In this work, BraTS 2017 dataset was employed to train and test the proposed network. Data imbalance was dealt with using a weighted cross entropy loss function. The problem of overfitting was handled through data augmentation. The proposed method achieved averaged dice scores of 0.883, 0.781 and 0.748 for whole tumor, tumor core and enhancing tumor respectively in the validation set and 0.877, 0.774, 0.757 respectively in the testing set.