English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We introduce "PatchMorph," an new stochastic deep learning algorithm tailoredfor unsupervised 3D brain image registration. Unlike other methods, our methoduses compact patches of a constant small size to derive solutions that cancombine global transformations with local deformations. This approach minimizesthe memory footprint of the GPU during training, but also enables us to operateon numerous amounts of randomly overlapping small patches during inference tomitigate image and patch boundary problems. PatchMorph adeptly handles worldcoordinate transformations between two input images, accommodating variances inattributes such as spacing, array sizes, and orientations. The spatialresolution of patches transitions from coarse to fine, addressing both globaland local attributes essential for aligning the images. Each patch offers aunique perspective, together converging towards a comprehensive solution.Experiments on human T1 MRI brain images and marmoset brain images from serial2-photon tomography affirm PatchMorph's superior performance.

PatchMorph: A Stochastic Deep Learning Approach for Unsupervised 3D Brain Image Registration with Small Patches