Medical image registration using deep neural networks: A comprehensive review. (October 2020)

• Record Type: Journal Article
• Authors: Boveiri, Hamid Reza; Khayami, Raouf; Javidan, Reza; Mehdizadeh, Alireza
• Is Part Of: Computers & electrical engineering. Volume 87(2020)
• Publication Date: 2020-10
• Subjects: Convolutional neural network (CNN) -- Deep learning -- Deep reinforcement learning -- Deformable registration -- Generative adversarial network (GAN) -- Image-guided intervention -- Medical image registration -- One-shot registration -- Precision medicine -- Stacked auto-encoders (SAEs); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106767
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A comprehensive review on the literature of medical image registration using deep neural networks is presented. The review is systematic and encompasses all the related works previously published in the field. Key concepts, statistical analysis, confining challenges, novelties, techniques, and prospective trends are surveyed. This review allows a deep insight for the readers active in the field who are investigating the state-of-the-art. Abstract: Image-guided interventions are saving the lives of a large number of patients where the image registration should indeed be considered as the most complex and complicated issue to be tackled. On the other hand, a huge progress in the field of machine learning has recently made by the possibility of implementing deep neural networks on the contemporary many-core GPUs. It has opened up a promising window to challenge with many medical applications in more efficient and effective ways, where the registration is not an exception. In this paper, a comprehensive review on the state-of-the-art literature known as medical image registration using deep neural networks is presented. The review is systematic and encompasses all the related works previously published in the field. Key concepts, statistical analysis from different points of view, confining challenges, novelties and main contributions, key-enabling techniques, future directions, and prospective trends all are discussed and surveyed in details in this comprehensive review. This review allows a deep understanding and insight for the readers active in the field who are investigating the state-of-the-art and seeking to contribute the future literature. Graphical abstract: Image, graphical abstract