Brain Tumor Segmentation Using Concurrent Fully Convolutional Networks and Conditional Random Fields

Guangyu Shen, Yi Ding, Tian Lan, Hao Chen, Zhiguang Qin

Abstract: We propose a concurrent Fully Convolutional Networks(CFCN) structure which contains three Fully Convolutional Networks(FCN). Gaussian filter, Mean filter and Median filter are chosen to pre-process the original multimodal MR images. Then, we fuse the results from three networks. Finally, a Fully Connected Conditional Random Field (Fully Connected CRF) is used to accomplish the post-processing, improving the model's ability of detecting minute structures. Our model was trained and evaluated on BRATS 2015 challenge dataset. The results show that our model provides promising segmentations.