PatchMorph: A Stochastic Deep Learning Approach for Unsupervised 3D
  Brain Image Registration with Small Patches
Henrik Skibbe Michal Byra Akiya Watakabe Tetsuo Yamamori Marco Reisert
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.