Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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○DOI references
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○Academic publication links
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✓Committers with academic emails
4 of 12 committers (33.3%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.2%) to scientific vocabulary
Repository
Image registration using pytorch
Basic Info
- Host: GitHub
- Owner: uncbiag
- License: other
- Language: Python
- Default Branch: master
- Size: 79.8 MB
Statistics
- Stars: 183
- Watchers: 19
- Forks: 29
- Open Issues: 10
- Releases: 0
Metadata Files
README.md
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iMagE Registration via autoMAtIc Differentiation
Mermaid is a registration toolkit making use of automatic differentiation for rapid prototyping. It is written in PyTorch and runs on the CPU and the GPU. Though GPU acceleration only becomes obvious for large images or 3D volumes. It supports registration of 1D (functions), 2D, and 3D images.
The easiest way to install a development version is to clone the repository, create a virtual conda environment and install it in there. This can be done as follows for a development installation:
conda create --name mermaid python=3.7 pip
conda activate mermaid
python setup.py develop
Or like this if you want to do a standard installation of mermaid:
conda create --name mermaid python=3.7 pip
conda activate mermaid
python setup.py install
There is also a nice documentation which can be built by executing
cd mermaid
cd docs
make html
You can also find the latest version on readthedocs:
https://mermaid.readthedocs.io/en/latest/index.html
In the near future there will also be a conda installer available. This will then allow installations via
conda install -c pytorch -c conda-forge -c anaconda -c uncbiag mermaid
There are already initial OSX/Linux versions available which can be installed via conda, but there are still some issues that need to be ironed out, so they might not be fully functional yet. Stay tuned.
Supported transformation models: * affinemap: map-based affine registration * diffusionmap: displacement-based diffusion registration * curvaturemap: displacement-based curvature registration * totalvariationmap: displacement-based total variation registration * svfmap: map-based stationary velocity field * svfimage: image-based stationary velocity field * svfscalarmomentumimage: image-based stationary velocity field using the scalar momentum * svfscalarmomentummap: map-based stationary velocity field using the scalar momentum * svfvectormomentumimage: image-based stationary velocity field using the vector momentum * svfvectormomentummap: map-based stationary velocity field using the vector momentum * lddmmshootingmap: map-based shooting-based LDDMM using the vector momentum * lddmmshootingimage: image-based shooting-based LDDMM using the vector momentum * lddmmshootingscalarmomentummap: map-based shooting-based LDDMM using the scalar momentum * lddmmshootingscalarmomentumimage: image-based shooting-based LDDMM using the scalar momentum * lddmmadaptsmoothermap: map-based shooting-based Region specific diffemorphic mapping, with a spatio-temporal regularizer * svfadaptsmoother_map: map-based shooting-based vSVF, with a spatio regularizer
Supported similarity measures: * ssd: sum of squared differences * ncc: normalize cross correlation * nccpositive: positive normalized cross-correlation * nccnegative: negative normalized cross-correlation * lncc: localized normalized cross correlation (multi-scale)
Supported solvers: * embedded RK4 * torchdiffeq: explicitadams, fixedadams, tsit5, dopri5, euler, midpoint, rk4
Optimizer: * support single/multi-scale optimizer * support SGD, l-BFGS and some limited support for adam
easyreg
We also wrote a companion python package, easyreg, which allows training deep networks for image registration based on the registration models available in mermaid. I.e., easyreg allows training networks that backpropagate through the mermaid transformation models (SVF, LDDMM, ...). You can have a look at the package here:
https://github.com/uncbiag/easyreg
Our other registration work
See https://github.com/uncbiag/registration for an overview of other registration approaches of our group and a short summary of how the approaches relate.
Publishing a release
To publish to conda, just tag a version in the format "major.minor.subminor". do not add a leading v. Travis will automatically push to the conda repository at uncbiag/mermaid. After this is done, to publish to pip, first locally run
pip wheel . -w dist
to build the wheel, then run
twine upload --repository pypi dist/mermaid-<version_number>--py3-none-any.whl
to publish to pypi.
Owner
- Name: uncbiag
- Login: uncbiag
- Kind: organization
- Repositories: 19
- Profile: https://github.com/uncbiag
GitHub Events
Total
- Watch event: 7
- Issue comment event: 2
Last Year
- Watch event: 7
- Issue comment event: 2
Committers
Last synced: almost 3 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Marc Niethammer | mn@c****u | 555 |
| zyshen | z****1@g****m | 157 |
| HastingsGreer | h****r@g****m | 9 |
| wildphoton | x****i@g****m | 9 |
| rkwitt | r****t@g****t | 7 |
| StephanieHaeger | s****r@c****u | 5 |
| Thomas Greer | t****r@b****u | 3 |
| Roland Kwitt | r****t@R****l | 2 |
| Xu Han | x****0@c****u | 2 |
| François-Xavier VIALARD | v****d@c****r | 1 |
| Hastings Greer | h****s@h****m | 1 |
| lintian | t****7@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 12 months ago
All Time
- Total issues: 25
- Total pull requests: 78
- Average time to close issues: about 2 months
- Average time to close pull requests: 2 days
- Total issue authors: 16
- Total pull request authors: 4
- Average comments per issue: 1.08
- Average comments per pull request: 0.0
- Merged pull requests: 74
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- marcniethammer (6)
- xhs400 (3)
- colddie (2)
- wildphoton (2)
- StephanieHaeger (1)
- riccardosamperna (1)
- thewtex (1)
- degilala (1)
- lintian-a (1)
- ChangQingScq (1)
- EricZimmermann (1)
- imzhangyd (1)
- thorwhalen (1)
- uchihatashi (1)
- cs123951 (1)
Pull Request Authors
- marcniethammer (67)
- HastingsGreer (9)
- dependabot[bot] (2)
- StephanieHaeger (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 2,434 last-month
- Total docker downloads: 632
- Total dependent packages: 0
- Total dependent repositories: 4
- Total versions: 2
- Total maintainers: 1
pypi.org: mermaid
Image registration toolbox based on pyTorch
- Homepage: https://github.com/uncbiag/mermaid
- Documentation: https://mermaid.readthedocs.io/
- License: Apache 2.0
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Latest release: 0.3.2
published over 4 years ago