Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review

TL;DR

This review paper comprehensively surveys the application of deep convolutional neural networks in brain MRI analysis, highlighting architectures, strategies, results, and future research directions in medical image processing.

Contribution

It provides an extensive overview of CNN architectures and techniques used in brain MRI analysis, summarizing recent advances and identifying future research directions.

Findings

CNN architectures have evolved significantly for brain MRI tasks

State-of-the-art strategies improve lesion and anatomical segmentation accuracy

Public datasets enable benchmarking of CNN performance in medical imaging

Abstract

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.