Big Data Mining and Analytics  2018, Vol. 1 Issue (1): 1-18    DOI: 10.26599/BDMA.2018.9020001
Applications of Deep Learning to MRI Images: A Survey
Jin Liu, Yi Pan, Min Li, Ziyue Chen, Lu Tang, Chengqian Lu, Jianxin Wang*
Jin Liu, Min Li, Lu Tang, Chengqian Lu, and Jianxin Wang are with the School of Information Science and Engineering, Central South University, Changsha 410083, China. E-mail: liujin06@mail.csu.edu.cn, limin@mail.csu.edu.cn, lutang@mail.csu.edu.cn, chengqlu@mail.csu.edu.cn.
Yi Pan and Ziyue Chen are with the Department of Computer Science, Georgia State University, Atlanta, GA30302, USA. E-mail: yipan@gsu.edu; zchen2@student.gsu.edu.

Abstract

Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tissue contrast, in recent years, Magnetic Resonance Imaging (MRI) has been attracting increasing attention. With the development of deep learning, many innovative deep learning methods have been proposed to improve MRI image processing and analysis performance. The purpose of this article is to provide a comprehensive overview of deep learning-based MRI image processing and analysis. First, a brief introduction of deep learning and imaging modalities of MRI images is given. Then, common deep learning architectures are introduced. Next, deep learning applications of MRI images, such as image detection, image registration, image segmentation, and image classification are discussed. Subsequently, the advantages and weaknesses of several common tools are discussed, and several deep learning tools in the applications of MRI images are presented. Finally, an objective assessment of deep learning in MRI applications is presented, and future developments and trends with regard to deep learning for MRI images are addressed.

Received: 26 July 2017      Published: 18 December 2017
Corresponding Authors: Jianxin Wang
 Fig. 1 An example of a deep feedforward network with an input layer, three hidden layers, and an output layer. 𝟏, AE$𝟐$,.., AE$𝑛$)."> Fig. 2 An example of a stacked autoencoder with n autoencoders (i.e., AE$𝟏$, AE$𝟐$,.., AE$𝑛$). Table?1 Some common and widely used deep learning tools. Table?2 Some deep learning tools applied in MRI images.