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DHR-Net:多模式医疗图像的动态协调注册网络

Xin Yang¹, Dongxue Li², Songyu Chen³

¹Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080, Heilongjiang, China; Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, Guangdong, China.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine

|July 9, 2025

概括

本研究介绍了多模式医疗图像注册的动态协调注册网络 (DHR-Net). DHR-Net提高了解剖学一致性和记录准确性,特别是对于心脏图像.

科学领域:

医疗成像医学成像
深度学习 (Deep Learning) 是一种深度学习.
计算解剖学的计算解剖学

背景情况:

深度学习显著提升了医疗图像记录,特别是在单模任务中.
多模体医学图像注册面临着诸如解剖学不一致性和不稳定的优化等挑战.

研究的目的:

提出一个端到端的框架,即动态协调注册框架 (DHR-Net),用于准确的多模式医疗图像注册.
在跨模态注册中增强解剖一致性和变形场优化.

主要方法:

DHR-Net采用一个级联式的两级架构,并配有连续的翻译和注册网络.
一个基于噪声对比估计的新损失函数最大化了相互信息的交叉模式一致性.
动态温度调整优化了特征对比度,并保留了高频细节,以维护拓结构.

主要成果:

与现有方法相比,DHR-Net表现出卓越的注册准确性,变形场平滑性和跨模式稳定性.
该框架显著提高了心脏图像记录质量.
在保存解剖结构方面观察到异常的表现.

结论:

DHR-Net为多模式医疗图像注册的挑战提供了一个强大的解决方案.
拟议的方法显示出在医学成像中临床应用的巨大潜力.

关键词:

跨模式翻译是跨模式翻译.动态协调注册动态协调注册管理和管理数据集.医疗图像注册医疗图像注册

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