Authors: Qiushi Nie, Xiaoqing Zhang, Yan Hu, Mingdao Gong, Jiang Liu

Published on: March 25, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2403.16502

Summary

• What is new: A comprehensive review focusing on both traditional and deep learning-based medical image registration methods, with a special emphasis on recent advances in retinal image registration.
• Why this is important: Lack of systematic summarization of methodologies in existing medical image registration methods.
• What the research proposes: Providing a detailed, comparative review of traditional and deep learning methodologies in medical image registration, especially highlighting advancements and challenges in retinal image registration.
• Results: Insights and prospects for future research in medical and particularly retinal image registration are offered, along with a discussion on current challenges.

Technical Details

Technological frameworks used: Traditional and deep learning methodologies

Models used: Not specifically mentioned but involves a range of models used in medical image registration.

Data used: Diverse medical images under different conditions

Potential Impact

Healthcare providers, medical imaging software companies, and medical research institutions could significantly benefit from the advancements and insights provided.

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