  In recent years, deep convolutional neural networks (CNNs) have shown
record-shattering performance in a variety of computer vision problems, such as
visual object recognition, detection and segmentation. These methods have also
been utilised in medical image analysis domain for lesion segmentation,
anatomical segmentation and classification. We present an extensive literature
review of CNN techniques applied in brain magnetic resonance imaging (MRI)
analysis, focusing on the architectures, pre-processing, data-preparation and
post-processing strategies available in these works. The aim of this study is
three-fold. Our primary goal is to report how different CNN architectures have
evolved, discuss state-of-the-art strategies, condense their results obtained
using public datasets and examine their pros and cons. Second, this paper is
intended to be a detailed reference of the research activity in deep CNN for
brain MRI analysis. Finally, we present a perspective on the future of CNNs in
which we hint some of the research directions in subsequent years.
