深度学习
人工智能
计算机科学
领域(数学)
无监督学习
图像配准
深层神经网络
机器学习
数据科学
医学影像学
图像(数学)
自动化
人工神经网络
图像处理
工程类
机械工程
数学
纯数学
作者
Taisen Duan,Wenkang Chen,Meilin Ruan,Xuejun Zhang,Shaofei Shen,Weiyu Gu
标识
DOI:10.1088/1361-6560/ad9e69
摘要
Abstract In recent decades, medical image registration technology has undergone significant development, becoming one of the core technologies in medical image analysis. With the rise of deep learning, deep learning-based medical image registration methods have achieved revolutionary improvements in processing speed and automation, showing great potential, especially in unsupervised learning. This paper briefly introduces the core concepts of deep learning-based unsupervised image registration, followed by an in-depth discussion of innovative network architectures and a detailed review of these studies, highlighting their unique contributions. Additionally, this paper explores commonly used loss functions, datasets, and evaluation metrics. Finally, we discuss the main challenges faced by various categories and propose potential future research topics. This paper surveys the latest advancements in unsupervised deep neural network-based medical image registration methods, aiming to help active readers interested in this field gain a deep understanding of this exciting area.
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