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通过回归注册 (RbR):可解释和灵活的地图注册框架

Karthik Gopinath¹, Xiaoling Hu¹, Malte Hoffmann¹

¹Massachusetts General Hospital and Harvard Medical School.

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- )

|August 28, 2025

概括

通过回归注册 (RbR) 为神经成像图谱注册提供了一种灵活而强大的方法. 这种新的框架使用voxel关键点和各种变形模型准确地映射MRI扫描, 性能优于现有的方法.

科学领域:

神经成像
医学图像分析
机器学习

背景情况:

对于汇总人类神经成像数据而言,
目前的机器学习方法缺乏灵活性和可解释性.
基于关键点的方法是有前途的,但对非线性变换的准确性不足.

研究的目的:

引入回归注册 (RbR),这是一个新的,强大的,灵活的地图注册框架.
使MRI扫描能够准确地映射到一个共同的坐标框架.
提供一种适应各种变形模型并可用于预训练的方法.

主要方法:

预测每个voxel的地图坐标, 每一个作为一个关键点.
使用封闭式表达式快速适应多种变形模型 (同系,B-spline,Demons,二形).
包括强大的估计器如RANSAC来提高登记准确性.

主要成果:

在地图登记方面,RbR表现出高度的稳定性和灵活性.
与现有的基于关键点的方法相比,在多种变形模型中实现了更高的准确性.
在独立的公共神经成像数据集上进行验证.

结论:

对于神经成像而言, RbR 是一个重要的进步.
提供了MRI数据聚合的多功能和准确的解决方案.
在医疗图像分析中进行预培训和更广泛的应用.

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