  Accurate and reliable brain tumor segmentation is a critical component in
cancer diagnosis, treatment planning, and treatment outcome evaluation. Build
upon successful deep learning techniques, a novel brain tumor segmentation
method is developed by integrating fully convolutional neural networks (FCNNs)
and Conditional Random Fields (CRFs) in a unified framework to obtain
segmentation results with appearance and spatial consistency. We train a deep
learning based segmentation model using 2D image patches and image slices in
following steps: 1) training FCNNs using image patches; 2) training CRFs as
Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs
fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices.
Particularly, we train 3 segmentation models using 2D image patches and slices
obtained in axial, coronal and sagittal views respectively, and combine them to
segment brain tumors using a voting based fusion strategy. Our method could
segment brain images slice-by-slice, much faster than those based on image
patches. We have evaluated our method based on imaging data provided by the
Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015
and BRATS 2016. The experimental results have demonstrated that our method
could build a segmentation model with Flair, T1c, and T2 scans and achieve
competitive performance as those built with Flair, T1, T1c, and T2 scans.
