$\ exttt{NePhi}$：用于近似微分同胚医学图像配准的神经变形场
$\texttt{NePhi}$: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration
AI总结：
提出可泛化的神经变形模型NePhi，通过函数化表示变形实现近似微分同胚医学图像配准，在保证精度与正则性的同时显著降低内存消耗并提升推理速度。
摘要：
本工作提出了NePhi，一种可泛化的神经变形模型，能够产生近似微分同胚的变换。与基于学习的配准方法中占主导地位的基于体素的变换场不同，NePhi以函数形式表示变形，从而在训练和推理期间的内存消耗、推理时间、配准精度以及变换正则性的设计空间内带来了极大的灵活性。具体而言，NePhi：1）与基于体素的学习方法相比，所需内存更少；2）与仅依赖优化的现有基于神经变形的配准方法相比，通过预测潜在码提高了推理速度；3）通过实例优化提高了精度；4）展现出优异的变形正则性，这对于医学图像配准非常理想。我们在2D合成数据集以及真实3D医学图像数据集（例如肺部和大脑）上展示了NePhi的性能。结果表明，在单分辨率配准设置中，NePhi能够匹配基于体素表示的精度。对于多分辨率配准，我们的方法在通过实例优化匹配当前SOTA基于学习的配准方法的精度同时，将内存需求减少了五倍。
英文摘要：
This work proposes NePhi, a generalizable neural deformation model which results in approximately diffeomorphic transformations. In contrast to the predominant voxel-based transformation fields used in learning-based registration approaches, NePhi represents deformations functionally, leading to great flexibility within the design space of memory consumption during training and inference, inference time, registration accuracy, as well as transformation regularity. Specifically, NePhi 1) requires less memory compared to voxel-based learning approaches, 2) improves inference speed by predicting latent codes, compared to current existing neural deformation based registration approaches that \emph{only} rely on optimization, 3) improves accuracy via instance optimization, and 4) shows excellent deformation regularity which is highly desirable for medical image registration. We demonstrate the performance of NePhi on a 2D synthetic dataset as well as for real 3D medical image datasets (e.g., lungs and brains). Our results show that NePhi can match the accuracy of voxel-based representations in a single-resolution registration setting. For multi-resolution registration, our method matches the accuracy of current SOTA learning-based registration approaches with instance optimization while reducing memory requirements by a factor of five. Our code is available at https://github.com/uncbiag/NePhi.
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。
