https://github.com/nipy/nitransforms

a standalone fork of nipy/nibabel#656
https://github.com/nipy/nitransforms

Last synced: 1 day ago
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a standalone fork of nipy/nibabel#656

• Host: GitHub
• URL: https://github.com/nipy/nitransforms
• Owner: nipy
• License: mit
• Created: 2019-10-11T18:35:08.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2024-09-17T14:01:39.000Z (about 2 months ago)
• Last Synced: 2024-09-18T16:49:28.875Z (about 2 months ago)
• Language: Python
• Homepage: https://nitransforms.readthedocs.io
• Size: 205 MB
• Stars: 28
• Watchers: 7
• Forks: 16
• Open Issues: 32
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGES.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# NiTransforms

[![DOI](https://joss.theoj.org/papers/10.21105/joss.03459/status.svg)](https://doi.org/10.21105/joss.03459)

[![ISBI2020](https://img.shields.io/badge/doi-10.31219%2Fosf.io%2F8aq7b-blue.svg)](https://doi.org/10.31219/osf.io/8aq7b)

[![Deps & CI](https://github.com/nipy/nitransforms/actions/workflows/travis.yml/badge.svg)](https://github.com/nipy/nitransforms/actions/workflows/travis.yml)

[![CircleCI](https://circleci.com/gh/nipy/nitransforms.svg?style=svg)](https://circleci.com/gh/nipy/nitransforms)

[![codecov](https://codecov.io/gh/nipy/nitransforms/branch/master/graph/badge.svg)](https://codecov.io/gh/nipy/nitransforms)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/nipy/nitransforms/master?filepath=docs%2Fnotebooks%2F)

[![Docs](https://readthedocs.org/projects/nitransforms/badge/?version=latest)](http://nitransforms.readthedocs.io/en/latest/?badge=latest)

A development repo for [nipy/nibabel#656](https://github.com/nipy/nibabel/pull/656)

## About

Spatial transforms formalize mappings between coordinates of objects in biomedical images.

Transforms typically are the outcome of image registration methodologies, which estimate

the alignment between two images.

Image registration is a prominent task present in nearly all standard image processing

and analysis pipelines.

The proliferation of software implementations of image registration methodologies has

resulted in a spread of data structures and file formats used to preserve and communicate

transforms.

This segregation of formats precludes the compatibility between tools and endangers the

reproducibility of results.

We propose a software tool capable of converting between formats and resampling images

to apply transforms generated by the most popular neuroimaging packages and libraries

(AFNI, FSL, FreeSurfer, ITK, and SPM).

The proposed software is subject to continuous integration tests to check the

compatibility with each supported tool after every change to the code base.

Compatibility between software tools and imaging formats is a necessary bridge

to ensure the reproducibility of results and enable the optimization and evaluation

of current image processing and analysis workflows.

## Integration with *NiBabel*

*NiTransforms* started as a feature-repo spun off of *NiBabel*.

Shortly after starting with [nipy/nibabel#656](https://github.com/nipy/nibabel/pull/656), it became apparent that it was going to build up in a humongous PR nobody would be able to review as thoroughly as it would require.

Also, *NiTransforms* has many connections to BIDS/BIDS-Derivatives and its X5 format specification for transforms, which falls outside of the current scope of *NiBabel*.

The plan is to make it an isolated tool, and once it is pertinent, proceed with the integration into *NiBabel*.

Once this repository is ready for integration, we will define what can go into *NiBabel* (presumably everything, except perhaps some final details of the X5 implementation, although *NiBabel* will support the data structure at least logically).

This is to say that the chances that NiTransforms is integrated into NiBabel are high and scheduled to happen in ~2022 Q2.