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.
@article{arxiv.1712.03747,
title = {Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review},
author = {Jose Bernal and Kaisar Kushibar and Daniel S. Asfaw and Sergi Valverde and Arnau Oliver and Robert Martí and Xavier Lladó},
journal= {arXiv preprint arXiv:1712.03747},
year = {2019}
}