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.
Highlights
Artificial intelligence[1,2,3] is a field of computer science that was created in the 1950s and a thriving field with many practical applications and research hotspots
We provide a comprehensive review for the architectures of deep learning and their applications to Magnetic Resonance Imaging (MRI) images based on the above analysis
For most of the existing learning-based image registration methods, there is a great limitation with regard to the fact that they need a lot of known correspondences during the training process
Summary
Artificial intelligence[1,2,3] is a field of computer science that was created in the 1950s and a thriving field with many practical applications and research hotspots. Deep learning is an improvement in artificial neural networks, and a new field in machine learning research[19,20,21,22,23,24]. To obtain accurate image interpretation results, it is imperative to develop an automatic image interpretation system that includes many functions, such as image detection, image registration, image segmentation, and image classification To realize this system, many machine learning methods have been widely applied. Jin Liu et al.: Applications of Deep Learning to MRI ImagesW A Survey features, many researchers have applied deep learning architectures to the development of this automatic image interpretation system. In this survey, we focus on deep learning. An objective assessment about deep learning in MRI applications is presented, and future developments and trends are addressed for deep learning by using MRI images
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