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ARDMR:适应性递归推理和表示解,用于多式模式大变形注册

Yibo Hu¹, Qi Zhang¹, Ziqi Zhao¹

¹School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.

Medical image analysis

|October 25, 2025

概括

这项研究引入了自适应递归可变形注册 (ARDMR) 模型,用于改进多式联络医疗图像注册,这对于肝癌诊断至关重要. 在处理复杂的变形和强度差异时,ARDMR提高了准确性和稳定性.

科学领域:

医学图像分析医学图像分析
计算机视觉计算机视觉
机器学习机器学习

背景情况:

多模式医疗图像注册对于肝癌的诊断和治疗至关重要.
挑战包括不同成像方式之间的显著强度变化和大组织变形.
现有的基于学习的模型与这些多式联运注册复杂性作斗争.

研究的目的:

开发一个先进的多模式可变形登记模型 (ARDMR),解决模式差异和大变形问题.
为了提高医疗图像注册的准确性和稳定性,用于肝癌应用.
增强各种数据集上的注册模型的概括能力.

主要方法:

拟议的ARDMR模型将表示解与多层对比损失 (MCL) 纳入模式不变特征.
引入多尺度特征注册 (MSFR) 模块,通过整合多尺度特征和变形场来处理复杂的变形.
实施了自适应递归推理策略,以优化基于实时性能的注册规模和代.

主要成果:

在定性和定量评估中,ARDMR在最先进的方法中表现优越.
与VoxelMorph基线相比,在子相似系数 (DSC) 度量方面取得了2.5%-5%的改善.
在分布移动数据上表现出卓越的稳定性和概括性.

结论:

拟议的ARDMR模型有效地减轻了模式差异,并在多式联络医疗图像注册中处理大变形.
在注册准确性和稳定性方面,ARDMR提供了显著的改进,对于肝癌分析特别有价值.
适应性递归推理策略提高了模型的实际应用性和性能.

关键词:

适应性的递归推理推理.相反的学习学习.多式联网图像注册服务多式联网图像注册服务没有监督的可变形注册.

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