一种基于关键点的鲁棒且可解释的多模态配准深度学习框架
A Robust and Interpretable Deep Learning Framework for Multi-modal Registration via Keypoints
AI总结：
提出KeyMorph框架，利用自动检测的对应关键点通过可微闭式解实现多模态图像配准，增强鲁棒性和可解释性，并在3D脑MRI配准中超越现有方法。
摘要：
我们提出了KeyMorph，一种基于深度学习的图像配准框架，其依赖于自动检测对应的关键点。最先进的用于配准的深度学习方法通常对大的错位不够鲁棒，不可解释，并且没有纳入问题的对称性。此外，大多数模型在测试时只产生单一预测。我们的核心洞察解决了这些不足，即图像之间的对应关键点可以通过可微的闭式表达式用于获得最优变换。我们利用这一观察来驱动针对配准任务量身定制的关键点的端到端学习，而无需真实关键点的知识。该框架不仅带来了显著更鲁棒的配准，而且提供了更好的可解释性，因为关键点揭示了图像的哪些部分驱动了最终的对齐。此外，KeyMorph可以被设计为在图像平移下等变和/或对输入图像顺序对称。最后，我们展示了如何在测试时高效地计算多个形变场，对应于不同的变换变体，并以闭式形式给出。我们在解决多模态脑部MRI扫描的3D仿射和基于样条的配准中展示了所提出的框架。特别地，我们展示了配准精度超过了当前最先进的方法，尤其是在大位移的情况下。我们的代码可在https://github.com/alanqrwang/keymorph获取。
英文摘要：
We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not interpretable, and do not incorporate the symmetries of the problem. In addition, most models produce only a single prediction at test-time. Our core insight which addresses these shortcomings is that corresponding keypoints between images can be used to obtain the optimal transformation via a differentiable closed-form expression. We use this observation to drive the end-to-end learning of keypoints tailored for the registration task, and without knowledge of ground-truth keypoints. This framework not only leads to substantially more robust registration but also yields better interpretability, since the keypoints reveal which parts of the image are driving the final alignment. Moreover, KeyMorph can be designed to be equivariant under image translations and/or symmetric with respect to the input image ordering. Finally, we show how multiple deformation fields can be computed efficiently and in closed-form at test time corresponding to different transformation variants. We demonstrate the proposed framework in solving 3D affine and spline-based registration of multi-modal brain MRI scans. In particular, we show registration accuracy that surpasses current state-of-the-art methods, especially in the context of large displacements. Our code is available at https://github.com/alanqrwang/keymorph.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
