  Segmentation of focal (localized) brain pathologies such as brain tumors and
brain lesions caused by multiple sclerosis and ischemic strokes are necessary
for medical diagnosis, surgical planning and disease development as well as
other applications such as tractography. Over the years, attempts have been
made to automate this process for both clinical and research reasons. In this
regard, machine learning methods have long been a focus of attention. Over the
past two years, the medical imaging field has seen a rise in the use of a
particular branch of machine learning commonly known as deep learning. In the
non-medical computer vision world, deep learning based methods have obtained
state-of-the-art results on many datasets. Recent studies in computer aided
diagnostics have shown deep learning methods (and especially convolutional
neural networks - CNN) to yield promising results. In this chapter, we provide
a survey of CNN methods applied to medical imaging with a focus on brain
pathology segmentation. In particular, we discuss their characteristic
peculiarities and their specific configuration and adjustments that are best
suited to segment medical images. We also underline the intrinsic differences
deep learning methods have with other machine learning methods.
