A fast and accurate fully automatic method for brain tumor segmentation which is competitive both in terms of accuracy and speed compared to the state of the art.

The method is based on deep neural networks (DNN) and learns features that are specific to brain tumor segmentation.

We present a new DNN architecture which exploits both local features as well as more global contextual features simultaneously.

Using a GPU implementation and a convolutional output layer, the model is an order of magnitude faster than other state of the art methods.

Introduces a novel cascaded architecture that allows the system to more accurately model local label dependencies.