Medical image registration using deep neural networks: A comprehensive review
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Previous reviews lack a summary of public datasets for benchmarking registration algorithms.
“On one hand, these reviews lack a summary of the public dataset for benchmarking registration algorithms.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [19]
The cited review does summarize datasets used to evaluate registration approaches. Its Literature Review Analysis (p. 35) lists frequently used datasets, distinguishes private datasets from the others, and presents their frequency of use in Figure 35. That is contrary to describing this review as lacking a dataset summary. (LLM, conf=0.84)
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Experimental — accuracy not yet measuredPrevious reviews about deep learning-based medical image registration literature have been published.
“Though previous reviews about deep learning-based medical image registration literature have been published (19–22), there are still some deficiencies.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [19]
Some details of the selected literature were missing in previous reviews.
“On the other hand, some details of the selected literature were missing.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [19]
The Reference Gathering Methodology (p. 3) says the authors found that their initial search overlooked some references, but it does not say that previous reviews omitted details of their selected literature. Searches for previous reviews and missing literature details found no passage in our copy addressing that claim. (LLM, conf=0.72)
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- Authors
- Boveiri, H. R., Khayami, R., Javidan, R., & Mehdizadeh, A.
- Affiliations
- Shiraz University of Technology (IR)
- Shiraz University of Medical Sciences (IR)
- Corresponding author
- Boveiri, Hamid Reza
- Venue
- Computers & Electrical Engineering
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 87
- Pages
- 106767
- ISSN
- 0045-7906 (ISSN-L)
- OpenAlex
- W3045889720
- Cited by
- 179
- References
- 119
- Citations by year
- 2026: 11 · 2025: 36 · 2024: 42 · 2023: 30 · 2022: 35 · 2021: 20 · 2020: 4
- FWCI
- 8.35
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Computers & Electrical Engineering (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- medical image registration, deep neural network, image-guided interventions, GPU acceleration, future research directions
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