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

By José Bernal, Kaisar Kushibar, Daniel Asfaw, Sergi Valverde, Arnau Oliver, Robert Marti, Xavier Lladó. 2017

385 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Neurology

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In Biomedical Engineering

Stéphane Canu

In Computer Vision and Pattern Recognition

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Citations in the corpus

A review: Deep learning for medical image segmentation using multi-modality fusion (2019, background): “[5] gave an overview in deep CNN for brain MRI analysis.”
Deep learning based brain tumor segmentation: a survey (2020, methodology): “State of the art survey on MRI brain tumor segmentation [17] MRI 2013 Summary of segmentation methods before 2013 A survey of MRI-based brain tumor segmentation methods [18] Tsinghua Science and Technology 2014 Survey of methods used for brain MRI segmentation before 2014 A survey on deep learning i”
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, background): “More recently, deep learning (DL), as a newly emerging ML methodology, has made a big leap in the domain of medical imaging (Bernal et al., 2018; J. Liu et al., 2018; Lundervold and Lundervold, 2018; Razzak et al., 2018; D. Wen et al., 2018).”