https://github.com/raamana/niatlas

Atlas methods and classes for neuroimaging
https://github.com/raamana/niatlas

atlas neuroimaging neuroimaging-analysis neuroscience nibabel nilabels nilearn nipy parcellation roi-analysis roi-segmentation template

Last synced: 5 months ago
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Atlas methods and classes for neuroimaging

• Host: GitHub
• URL: https://github.com/raamana/niatlas
• Owner: raamana
• License: other
• Created: 2019-07-11T13:48:24.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-26T20:47:40.000Z (over 3 years ago)
• Last Synced: 2024-04-24T14:30:07.061Z (about 2 years ago)
• Topics: atlas, neuroimaging, neuroimaging-analysis, neuroscience, nibabel, nilabels, nilearn, nipy, parcellation, roi-analysis, roi-segmentation, template
• Language: Makefile
• Size: 21.5 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE

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README

          
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niatlas

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Atlas classes and methods for neuroimaging

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This is a placeholder repository for potential development of ``nibabel``-like python package that is intended to make the management of Atlases for neuroimaging seamless, in all their diverse use-cases. We will collect and link to various relevant resources to facilitate this discussion and project development.

As I currently envision it, a decent ``niatlas`` package would offer the following features (consider it a **wishlist**):

 - easy access (including I/O) to all the popular atlases just by their name, both volumetric- and surface-based.

 - a well-defined data structure that provides, not only the parcellations, but also all the relevant ``meta-data``, such as

    - the source of atlas, in terms of modalities and tha processes that generated it

    - methods defining the parcellation,

    - number, names and centroids of ROIs, along with resolution and dimensions

    - whether it is intended to be used as a volumetric or surface atlas,

    - **domain tags** that identify which **age-group** this atlas would be ideal for, along with other info related to target population

    - etc

 - several convenience methods to perform the common operations on atlases including but not limited to

    - computing ROI-based statistics

    - masking operations

 - Methods to obtain different variations of the same atlas e.g.

    - resampling the parcellation to a different resolution, or to different dimensions (that respects the internal parcellations)

    - scale control in terms of number or size of ROIs i.e. methods for subdividing or clustering existing ROIs

    - conversion to different spaces, such as between volumetric and surface-oriented spaces

    - conversion between atlas- and subject-spaces

 - visualization routines for all the common analyses needs

 - easy integration and high interoperability with popular tools and ecosystems

Some prior discussion on potential data structures for Atlas object and uniform access to parcellations at nilearn `here `_

Prior Art (software)

~~~~~~~~~~~~~~~~~~~~~~~

 - nilearn ``fetch_{atlas}`` `utilities `_

 - ` nilabels `_ :  tools to automate simple manipulations and measurements of medical image segmentations

 - `AtlasReader` to generate coordinate tables and region labels from statistical MRI images : https://github.com/miykael/atlasreader

 - pysurfer visualization tool: https://pysurfer.github.io/auto_examples/index.html

 - python package for subparcellation of fsaverage etc: https://github.com/miykael/parcellation_fragmenter

 - shell scripts to move from atlas space to subject space https://github.com/faskowit/multiAtlasTT

Resources - atlas collections

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 - Different atlases in MNI space: http://www.lead-dbs.org/helpsupport/knowledge-base/atlasesresources/cortical-atlas-parcellations-mni-space/

 - Different atlases from Thomas Yeo's lab 

    - set 1: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation

    - set 2: https://github.com/ThomasYeoLab/CBIG/tree/master/data

 - `BALSA (Brain Analysis Library of Spatial maps and Atlases) `_ is a database for hosting and sharing neuroimaging and neuroanatomical datasets for human and primate species.

 - Brain signature patterns, atlases of regions, and meta-analysis masks from Tor Wager's lab: https://github.com/canlab/Neuroimaging_Pattern_Masks