提出可微分相关比损失，提升多模态图像配准精度。
Correlation Ratio for Unsupervised Learning of Multi-modal Deformable Registration
- 用Parzen窗近似实现相关比可微，支持深度网络反向传播
- 在神经影像数据集上优于传统互信息等相似性度量
- 构建贝叶斯框架分析正则项与相似性权衡的影响
近年来，无监督学习在可变形图像配准领域备受关注。该方法通过移动图像与固定图像对，结合图像相似性度量和形变正则化项构成损失函数训练注册网络。针对多模态配准任务，相关比虽为经典相似性度量，却在现有深度学习方法中未被充分探索。本文提出一种可微分相关比作为学习型多模态可变形配准的损失函数。通过帕尔岑窗近似扩展传统非可微实现，使梯度可回传至深度神经网络。我们在一个多模态神经影像数据集上验证了该方法的有效性，并建立贝叶斯训练框架，研究形变正则化项与相似性度量（包括互信息及本文提出的相关比）之间的权衡对配准性能的影响。
原文摘要 · Abstract (English)
In recent years, unsupervised learning for deformable image registration has been a major research focus. This approach involves training a registration network using pairs of moving and fixed images, along with a loss function that combines an image similarity measure and deformation regularization. For multi-modal image registration tasks, the correlation ratio has been a widely-used image similarity measure historically, yet it has been underexplored in current deep learning methods. Here, we propose a differentiable correlation ratio to use as a loss function for learning-based multi-modal deformable image registration. This approach extends the traditionally non-differentiable implementation of the correlation ratio by using the Parzen windowing approximation, enabling backpropagation with deep neural networks. We validated the proposed correlation ratio on a multi-modal neuroimaging dataset. In addition, we established a Bayesian training framework to study how the trade-off between the deformation regularizer and similarity measures, including mutual information and our proposed correlation ratio, affects the registration performance.
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