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关于医疗图像注册中的深度学习的调查:新技术,不确定性,评估指标,以及其他方面

Junyu Chen¹, Yihao Liu², Shuwen Wei²

¹Department of Radiology and Radiological Science, Johns Hopkins School of Medicine, MD, USA.

Medical image analysis

|November 29, 2024

概括

深度学习显著推进了医疗图像注册,提高了准确性和应用. 本次审查涵盖了新的网络设计,损失函数和不确定性估计,以改进医疗成像任务.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

在过去的十年里,深度学习彻底改变了医学图像记录.
早期的方法,如回归和基于U-Net的网络奠定了基础.
在相似性测量,变形规范化,网络架构和不确定性估计方面取得了进展.

研究的目的:

提供关于基于深度学习的医学图像注册的最新进展的全面概述.
引入核心概念,创新架构,损失函数和不确定性估计方法.
探索医学成像中的评估指标和实际应用.

主要方法:

关于深度学习用于医疗图像注册的最新文献的综述.
分析新型网络架构和专门的损失函数.
讨论不确定性估计技术和评估指标.

主要成果:

深度学习模型在图像注册的各个方面都显示出了显著的改进.
进步促进了诸如地图集构建,细分,运动估计和2D-3D注册等应用.
新的技术提高了准确性,并提供了不确定性量化.

结论:

基于深度学习的注册为医学成像提供了强大的工具.
对网络设计,损失函数和不确定性的持续研究至关重要.
未来的前景包括更广泛的应用和更强大的注册方法.

关键词:

深度神经网络是一种深度神经网络.图像的注册图像的注册医学成像医学成像

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