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Peizhi Chen¹, Jialan Wang¹, Yifan Guo¹

¹College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China.

Current medical imaging

|October 24, 2025

概括

视觉转换器 (ViT) 在4D-CT扫描无监督可变形图像注册方面优于卷积神经网络 (CNN). 维特在捕捉全球特征方面表现出色,与CNN相比,导致更高的注册准确性.

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:

可变形图像的注册对于医学图像分析至关重要.
目前的无监督方法使用卷积神经网络 (CNN) 和视觉转换器 (ViT).
CNN在本地特征提取方面表现出色,而ViT在全球特征提取方面表现更好.

研究的目的:

为了比较CNN和ViT架构在无监督可变形图像注册中的性能.
评估CNN和ViT统一登记框架的有效性.

主要方法:

制定了一个统一的注册框架.
在这个框架内,CNN和ViT架构都得到了评估.
实验使用4D-CT成像数据进行.

主要成果:

基于ViT的可变形图像注册表现出比基于CNN的方法更高的性能.
结果表明,ViT在这种应用中具有显著的优势.

结论:

视觉转换器架构在无监督可变形注册方面比CNN更有效.
这一发现对于4D-CT数据分析尤为重要.
该研究强调了ViTs在推进医疗图像注册方面的潜力.

关键词:

卷积神经网络是一种卷积神经网络.可变形图像的注册方式评价评价评价呼吸运动建模呼吸系统运动建模视力变压器. 视力变压器. 视力变压器.

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