Medical Image Registration Using Deep Neural Networks: A Comprehensive Review

Abstract

Image-guided interventions are saving the lives of a large number of patients where the image registration problem should indeed be considered as the most complex and complicated issue to be tackled. On the other hand, the recently huge progress in the field of machine learning made by the possibility of implementing deep neural networks on the contemporary many-core GPUs opened up a promising window to challenge with many medical applications, 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, confiding 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.

45 Pages, 39 Figures, 10 Tables, 2 Appendixes

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• Multi-Atlas Segmentation and Spatial Alignment of the Human Embryo in First Trimester 3D Ultrasound
• FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentation
• Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms
• Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods