Version 1
: Received: 1 September 2023 / Approved: 4 September 2023 / Online: 5 September 2023 (03:51:29 CEST)
How to cite:
Hammoudeh, A.; Dupont, S. Deep Learning in Medical Image Registration: Introduction and Survey. Preprints2023, 2023090223. https://doi.org/10.20944/preprints202309.0223.v1
Hammoudeh, A.; Dupont, S. Deep Learning in Medical Image Registration: Introduction and Survey. Preprints 2023, 2023090223. https://doi.org/10.20944/preprints202309.0223.v1
Hammoudeh, A.; Dupont, S. Deep Learning in Medical Image Registration: Introduction and Survey. Preprints2023, 2023090223. https://doi.org/10.20944/preprints202309.0223.v1
APA Style
Hammoudeh, A., & Dupont, S. (2023). Deep Learning in Medical Image Registration: Introduction and Survey. Preprints. https://doi.org/10.20944/preprints202309.0223.v1
Chicago/Turabian Style
Hammoudeh, A. and Stéphane Dupont. 2023 "Deep Learning in Medical Image Registration: Introduction and Survey" Preprints. https://doi.org/10.20944/preprints202309.0223.v1
Abstract
Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers.
Keywords
deep learning; medical images; image registration; medical image analysis; survey; review
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
