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

arXiv 2312.06958 ↗First posted 12 Dec 2023cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 12 Dec 2023.

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References · 10

  1. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 662
  2. 2020SynthMorph: learning contrast-invariant registration without acquired imagescited by 140
  3. 2020Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networkscited by 136
  4. 2019Deep Learning in Medical Image Registration: A Surveycited by 124
  5. 2018Unsupervised Learning for Fast Probabilistic Diffeomorphic Registrationcited by 116
  6. 2018Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registrationcited by 90
  7. 2017Label-driven weakly-supervised learning for multimodal deformable image registrationcited by 34
  8. 2023Unsupervised 3D registration through optimization-guided cyclical self-trainingcited by 7
  9. 2022MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latentcited by 4
  10. 2022Deep Neural Patchworks: Coping with Large Segmentation Taskscited by 2

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