mlsanityData to 14 Oct 2026

Deep learning in medical image registration: introduction and survey

arXiv 2309.00727 ↗First posted 1 Sep 2023eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 41 of its 111 references that are in the map. First posted on arXiv 1 Sep 2023.

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41 of 111 references.

References · 41

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29 more
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