We introduce "PatchMorph," an new stochastic deep learning algorithm tailored
for unsupervised 3D brain image registration. Unlike other methods, our method
uses compact patches of a constant small size to derive solutions that can
combine global transformations with local deformations. This approach minimizes
the memory footprint of the GPU during training, but also enables us to operate
on numerous amounts of randomly overlapping small patches during inference to
mitigate image and patch boundary problems. PatchMorph adeptly handles world
coordinate transformations between two input images, accommodating variances in
attributes such as spacing, array sizes, and orientations. The spatial
resolution of patches transitions from coarse to fine, addressing both global
and local attributes essential for aligning the images. Each patch offers a
unique perspective, together converging towards a comprehensive solution.
Experiments on human T1 MRI brain images and marmoset brain images from serial
2-photon tomography affirm PatchMorph's superior performance.

PatchMorph 是一种新型的随机深度学习算法，用于无监督的三维脑图像配准。与其他方法不同，我们的方法使用了具有恒定小尺寸的紧凑补丁，以得到既能够结合全局变换又能进行局部变形的解决方案。这种方法在训练过程中最小化了 GPU 的内存占用，同时可以在推理过程中处理大量随机重叠的小补丁，从而减轻图像和补丁边界问题。PatchMorph 能够处理两个输入图像之间的世界坐标变换，适应间距、数组大小和方向等属性的变化。补丁的空间分辨率从粗到细，同时处理全局和局部属性，以实现图像对齐所必需的功能。每个补丁都提供了独特的视角，并一起收敛到一个全面的解决方案。对人类 T1 MRI 脑图像和序列 2-photon 层析成像的 marmoset 脑图像的实验证明了 PatchMorph 的出色性能。