  Gliomas are the most common malignant brain tumors that are treated with
chemoradiotherapy and surgery. Magnetic Resonance Imaging (MRI) is used by
radiotherapists to manually segment brain lesions and to observe their
development throughout the therapy. The manual image segmentation process is
time-consuming and results tend to vary among different human raters.
Therefore, there is a substantial demand for automatic image segmentation
algorithms that produce a reliable and accurate segmentation of various brain
tissue types. Recent advances in deep learning have led to convolutional neural
network architectures that excel at various visual recognition tasks. They have
been successfully applied to the medical context including medical image
segmentation. In particular, fully convolutional networks (FCNs) such as the
U-Net produce state-of-the-art results in the automatic segmentation of brain
tumors. MRI brain scans are volumetric and exist in various co-registered
modalities that serve as input channels for these FCN architectures. Training
algorithms for brain tumor segmentation on this complex input requires large
amounts of computational resources and is prone to overfitting. In this work,
we construct FCNs with pretrained convolutional encoders. We show that we can
stabilize the training process this way and achieve an improvement with respect
to dice scores and Hausdorff distances. We also test our method on a privately
obtained clinical dataset.
