用学习的基函数建模局部功能依赖，提升多模态医学图像配准精度。
New multimodal similarity measure for image registration via modeling local functional dependence with linear combination of learned basis functions
- 通过联合学习基函数建模局部像素值间的功能依赖关系。
- 在三个数据集上优于主流基准方法和早期功能依赖方法。
- 基于卷积实现，可在GPU上高效计算，适合临床应用。
多模态医学图像的可变形配准在诸多应用中至关重要，但挑战在于不同模态图像捕捉组织信息的方式差异大，难以建立稳健的重叠度量。本文探索基于注册图像强度值间功能依赖的相似性度量。尽管全局功能依赖过于严格，但先前研究表明在足够小的局部上下文中该方法表现良好。本文验证了这一发现，并进一步提出通过联合学习的线性基函数模型来建模局部功能依赖。该度量可通过卷积高效实现，支持GPU加速。我们发布了易用工具包，在三个数据集上的实验表明其性能优于现有主流基线方法及早期功能依赖方法。
原文摘要 · Abstract (English)
The deformable registration of images of different modalities, essential in many medical imaging applications, remains challenging. The main challenge is developing a robust measure for image overlap despite the compared images capturing different aspects of the underlying tissue. Here, we explore similarity metrics based on functional dependence between intensity values of registered images. Although functional dependence is too restrictive on the global scale, earlier work has shown competitive performance in deformable registration when such measures are applied over small enough contexts. We confirm this finding and further develop the idea by modeling local functional dependence via the linear basis function model with the basis functions learned jointly with the deformation. The measure can be implemented via convolutions, making it efficient to compute on GPUs. We release the method as an easy-to-use tool and show good performance on three datasets compared to well-established baseline and earlier functional dependence-based methods.
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