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SynMSE:一种多式相似性评估器,用于在无监督可变形多式医疗图像注册中复杂分布差异的多式相似性评估

Jingke Zhu¹, Boyun Zheng², Bing Xiong¹

¹Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing 101400, China.

Medical image analysis

|May 1, 2025

概括

本研究介绍了SynMSE,这是一种用于无监督可变形多式联络医疗图像记录的新型相似性评估器. 尽管存在域间隙和运动变化,SynMSE有效地对齐图像,实现了最先进的结果.

科学领域:

医疗图像分析医学图像分析
计算机视觉计算机视觉计算机视觉
生物医学工程生物医学工程

背景情况:

无监督的可变形多式联络医疗图像注册面临诸如多式联络领域差距,解剖异质性和运动变异性等挑战.
这些因素导致灰度差异,阻碍了不同成像模式之间的精确对齐.
现有的注册方法往往不足以应对这些复杂的情况.

研究的目的:

提出SynMSE,一种用于无监督可变形医疗图像注册的新型多式联络相似性评估器.
开发一个增强现有注册框架的插件播放模块.
为应对领域差距,解剖学异质性和运动变异性所带来的挑战.

主要方法:

通过使用随机转换来训练 SynMSE,以模拟空间错位.
使用结构受约束的发电机来建模灰度分布差异.
该方法强调空间对齐,并减轻分布变化.

主要成果:

SynMSE在多个基准数据集 (Learn2Reg 2022 CT-MR腹部,临床宫CT-MR,CuRIOUS MR-US大脑) 上实现了最先进的性能.
拟议的相似性评估器有效地处理复杂的注册场景.
实验结果证明了SynMSE的稳定性和准确性.

结论:

SynMSE提供了一个强大的解决方案,用于无监督的可变形多式联络医疗图像记录.
由于 SynMSE 的 plug-and-play 特性,可以无地集成到各种注册框架中.
这项工作通过为准确的医疗图像对齐提供了强大的工具,从而推动了该领域的发展.

关键词:

分布差异的差异性医疗图像注册医疗图像注册多模式相似性评估器

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