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neuroharmony

A new tool for harmonizing volumetric MRI data from unseen scanners (Garcia-Dias et al. 2020)

https://github.com/garciadias/neuroharmony

Science Score: 33.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: sciencedirect.com
  • ✓
    Committers with academic emails
    1 of 2 committers (50.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.0%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

A new tool for harmonizing volumetric MRI data from unseen scanners (Garcia-Dias et al. 2020)

Basic Info
Statistics
  • Stars: 16
  • Watchers: 5
  • Forks: 10
  • Open Issues: 4
  • Releases: 0
Created over 6 years ago · Last pushed over 3 years ago
Metadata Files
Readme License

README.rst 

Neuroharmony: A tool for harmonizing volumetric MRI data from unseen scanners
=============================================================================

The model presented in `Garcia-Dias, et
al. (2020) `__.

Documentation
-------------

`neuroharmony.readthedocs.io `__


Install Neuroharmony.
---------------------

::

   pip install neuroharmony

Introduction
------------

The increasing availability of magnetic resonance imaging (MRI) datasets is boosting the interest in the application
of machine learning in neuroimaging. A key challenge to the development of reliable machine learning models, and
their translational implementation in real-word clinical practice, is the integration of datasets collected using
different scanners. Current approaches for harmonizing multi-scanner data, such as the ComBat method, require a
statistically representative sample, and therefore are not suitable for machine learning models aimed at clinical
translation, where the focus is on the assessment of individual scans from previously unseen scanners. To overcome
this challenge, Neuroharmony uses image quality metrics (IQMs, i.e. intrinsic characteristics that can be extracted
from individual images without requiring a statistically representative sample and any extra information about the
scanners) to harmonize single images from unseen/unknown scanners.

 .. image:: docs/_static/article.png
   :width: 700
   :target: https://doi.org/10.1016/j.neuroimage.2020.117127
   :alt: Front page of the article "Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners".

Run Tests
---------

python -m pytest tests/

Owner

  • Name: Rafael Garcia-Dias
  • Login: garciadias
  • Kind: user
  • Location: London
  • Company: King's College London

PhD in Machine Learning applied to Astrophysics, working on Foundational Models for Healthcare at King's College London.

GitHub Events

Total
  • Watch event: 2
Last Year
  • Watch event: 2

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 81
  • Total Committers: 2
  • Avg Commits per committer: 40.5
  • Development Distribution Score (DDS): 0.049
Past Year
  • Commits: 4
  • Committers: 1
  • Avg Commits per committer: 4.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
R. Garcia-Dias r****d@g****m 77
lea k****1@k****k 4
Committer Domains (Top 20 + Academic)
kcl.ac.uk: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 3
  • Total pull requests: 4
  • Average time to close issues: 4 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 3
  • Total pull request authors: 3
  • Average comments per issue: 3.67
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 2
Past Year
  • Issues: 0
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: about 6 hours
  • 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: 0
View more stats
Top Authors
Issue Authors
  • swarrington1 (1)
  • mcraig-ibme (1)
  • martahedl (1)
Pull Request Authors
  • dependabot[bot] (2)
  • asantentata (1)
  • yarikoptic (1)
Top Labels
Issue Labels
bug (1)
Pull Request Labels
dependencies (2)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 26 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 13
  • Total maintainers: 1
pypi.org: neuroharmony

A tool to perform Freesurfer volume Harminization in unseen scanner.

  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 26 Last month
Rankings
Dependent packages count: 10.0%
Forks count: 11.4%
Average: 15.8%
Stargazers count: 17.1%
Downloads: 18.8%
Dependent repos count: 21.7%
Maintainers (1)
garcia-dias
Last synced: 11 months ago

Dependencies

docs/requirements.txt pypi
  • Sphinx ==3.1.2
  • joblib ==0.16.0
  • matplotlib ==3.2.2
  • neuroharmony *
  • numpy ==1.16.1
  • numpydoc *
  • pandas ==1.0.1
  • seaborn ==0.10.1
  • sphinx_gallery ==0.7.0
  • sphinx_rtd_theme ==0.5.0
requirements.txt pypi
  • joblib >=0.14.1
  • numpy >=1.16.1
  • pandas >=1.1.4
  • pytest >=5.1.3
  • requests >=2.25.1
  • scikit_learn >=0.22.2.post1
  • scipy >=1.2.0
  • tqdm >=4.31.1