Deep learning in medical image registration: introduction and survey

• URL: http://arxiv.org/abs/2309.00727v2
• Date: Wed, 10 Jan 2024 13:01:36 GMT
• Title: Deep learning in medical image registration: introduction and survey
• Authors: Ahmad Hammoudeh, St\'ephane Dupont
• Abstract summary: This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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