Deep learning image registration library for PyTorch

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Deep LearningTorchIR
Overview

TorchIR: Pytorch Image Registration

TorchIR is a image registration library for deep learning image registration (DLIR). I have integrated several ideas for image registration.

The current version lacks a document, but I have included quite a descriptive tutorial using MNIST data as an example. The example experiments are light-weight and should run on any CPU, although running it on a GPU will increase the speed. The notebook contains a tutorial using MNIST data as an example. Although the code runs faster on a GPU, this tutorial is small enough to run on CPU.

For the tutorial I rely on PyTorch Lightning, which can be installed via:

pip install pytorch-lightning

The pytorch-lightning trainer modules automatically create tensorboard log files. I store them in the ./output/lightning_logs directory. Simply inspect them using:

tensorboard --logdir=./output/lightning_logs

If you use this code for your publications, don't forget to cite my work ;)

[1] Bob D. de Vos, Floris F. Berendsen, Max A. Viergever, Marius Staring and Ivana Išgum, "End-to-end unsupervised deformable image registration with a convolutional neural network," in Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, Cham, 2017. p. 204-212, doi: 10.1007/978-3-319-67558-9_24 https://link.springer.com/chapter/10.1007%2F978-3-319-67558-9_24

[2] Bob D. de Vos, Floris F. Berendsen, Max A. Viergever, Hessam Sokooti, Marius Staring and Ivana Išgum "A deep learning framework for unsupervised affine and deformable image registration," Medical image analysis, vol. 52, pp. 128-143, Feb. 2019, doi: 10.1016/j.media.2018.11.010 https://www.sciencedirect.com/science/article/pii/S1361841518300495

Please note that the code is still under heavy development and I'd really love your input.

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Comments
  • Rigid Transformation are not defined in RigidIRNet

    Rigid Transformation are not defined in RigidIRNet

    Respected, Kindly, provide the solution, how to create the class of Rigid transformer in transformer.py. Create the class of RigidIRNet in "https://github.com/BDdeVos/TorchIR/blob/main/torchir/networks/globalnet.py" .

    Thanks.

    opened by Paluck1Arora2 0
  • No networks Module on PyPi

    No networks Module on PyPi

    PyPi Package torchir==0.1 Does Not Include Networks Package Reproduce: pip3 install torchir in python: from torchir.networks.globalnet import ConvBlock

    opened by pweibert 0
  • Using color images as input for the network

    Using color images as input for the network

    Hello,

    I am trying to adapt your work for the alignment of ophthalmological retina images using 3-channel color images. Which changes are necessary to use the framework for this kind of data?

    Kind regards and thanks for your work on image registration!

    opened by AgenoDrei 0
  • Rigid transformation parameters not equal

    Rigid transformation parameters not equal

    Hi, I used torchio to generate some moved 3d image ,which by applying the translation(pixelwise) and rotation(degree) to the still image(without motion), and use the network RigidIRNet to estimate this translation and rotation parameter.

    Then I found that although the NCC loss are decreased and the warped image is acceptable, the estimated parameter have a big different compared with the one that I appied beforehand.

    May I ask why is the issue for this weird situation ?

    opened by jeremysong1106 0
Releases(version_01)
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Bob de Vos
Bob de Vos
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