A Checklist of Quality Control For Making Atlases and Templates
Poster No:
1803
Submission Type:
Abstract Submission
Authors:
Daniel Glen1, Paul Taylor2, Richard Reynolds3
Institutions:
1National Institute of Mental Health (NIMH), NIH, Bethesda, MD, 2National Institutes of Health, Bethesda, MD, 3NIMH, Bethesda, MD
First Author:
Co-Author(s):
Introduction:
While recent MRI projects have focused on quality control in structural and fMRI datasets, commonly used templates and atlases often lack proper scrutiny. This work introduces a checklist and tools within AFNI to standardize checks on these datasets for consistency, artifacts, and header properties. These tools and checklist are intended for both atlas creators and consumers of these reference datasets.
Methods:
Definitions: "Template" refers to a reference dataset with anatomical features and smoothly varying intensity, e.g. MNI152_2009c, often used for subject registration; the "template space" often defines the coordinate system for the final data analysis. An "atlas" is a parcellation of regions, usually with a single index value and name for each region, e.g. JulichBrain.
An overview of checklist features:
Formats
Datasets should be NIFTI with appropriate sform_code, qform_code (3=Talairach,4=MNI,5=Other).
Data type=16-bit signed integers for templates, 8 or 16-bit for atlases for memory conservation.
Provide additional information such as labels, space name, dataset version, general descriptions and citation info through header extensions (as in AFNI), JSON or other external files.
Coordinates and grids
Avoid excessive padding, but leave space around the brain for alignment
Ensure a useful,correct coordinate system exists with these requirements
Meaningful origin of (0,0,0) (AC, EBZ)
Use cardinal, non-oblique orientation
Left-right are correct and match between template and atlas
Template intensities
Intensities should be integers, within reasonable ranges, (often in the low thousands)
Zero values outside the head
Avoid hyperintensities
Symmetry
Nominally symmetric atlases and templates should be checked for asymmetries
ROI irregularities
Avoid stairsteps and holes in atlases from 2D drawing and multi-subject averaging by correcting with "modal smoothing" in volume or surface (3dLocalstat/SurfLocalstat)
Check for lost clusters-disconnected parts of regions (@ROI_decluster, IsoSurface)
ROI consistency
ROIs should be consistent within the set that are distributed; whole brain, cortex, segmentation and lobule masks should correspond within the ROIs
All labeled ROIs should be checked for existence; unlabeled ROIs are not included (@Atlasize)
ROI names
Names should exist for each index with no duplicates
Avoid punctuation (quotes,accents,...) to make scripting easier
Atlas space
Clearly identify the associated template
An overview of checklist features:
Formats
Datasets should be NIFTI with appropriate sform_code, qform_code (3=Talairach,4=MNI,5=Other).
Data type=16-bit signed integers for templates, 8 or 16-bit for atlases for memory conservation.
Provide additional information such as labels, space name, dataset version, general descriptions and citation info through header extensions (as in AFNI), JSON or other external files.
Coordinates and grids
Avoid excessive padding, but leave space around the brain for alignment
Ensure a useful,correct coordinate system exists with these requirements
Meaningful origin of (0,0,0) (AC, EBZ)
Use cardinal, non-oblique orientation
Left-right are correct and match between template and atlas
Template intensities
Intensities should be integers, within reasonable ranges, (often in the low thousands)
Zero values outside the head
Avoid hyperintensities
Symmetry
Nominally symmetric atlases and templates should be checked for asymmetries
ROI irregularities
Avoid stairsteps and holes in atlases from 2D drawing and multi-subject averaging by correcting with "modal smoothing" in volume or surface (3dLocalstat/SurfLocalstat)
Check for lost clusters-disconnected parts of regions (@ROI_decluster, IsoSurface)
ROI consistency
ROIs should be consistent within the set that are distributed; whole brain, cortex, segmentation and lobule masks should correspond within the ROIs
All labeled ROIs should be checked for existence; unlabeled ROIs are not included (@Atlasize)
ROI names
Names should exist for each index with no duplicates
Avoid punctuation (quotes,accents,...) to make scripting easier
Atlas space
Clearly identify the associated template
Results:
Fig. 1 shows good and bad examples of problems with atlases and templates for a generic atlas and template similar to issues we have seen in practice.
·Fig.1. Atlas Quality Control Examples
Conclusions:
AFNI has a long history of supporting and developing atlases and templates, from the early introduction of the Talairach atlas to the most recent atlases for macaque, marmoset and human brain. In that role, we curate these atlases to verify the properties for quality control. Here, we present a set of criteria and tools to promote proper implementations of these important resources for the neuroimaging community.
Modeling and Analysis Methods:
Methods Development 2
Segmentation and Parcellation
Neuroinformatics and Data Sharing:
Brain Atlases 1
Keywords:
Atlasing
Open Data
Open-Source Code
Open-Source Software
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
Which processing packages did you use for your study?
Provide references using APA citation style.
Cox RW (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29(3):162-173. doi:10.1006/cbmr.1996.0014
https://pubmed.ncbi.nlm.nih.gov/8812068/
Cox RW, Ashburner J, Breman H, Fissell K, Haselgrove C, Holmes CJ, Lancaster JL, Rex DE, Smith SM, Woodward JB, Strother SC (2004). A (sort of) new image data format standard: NiFTI-1. Presented at the 10th Annual Meeting of the Organization for Human Brain Mapping.
Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL, et al. (2011). Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54, 313–327.
Taylor PA, Glen DR, Reynolds RC, Basavaraj A, Moraczewski D, Etzel JA (2023). Editorial: Demonstrating quality control (QC) procedures in fMRI. Front. Neurosci. 17:1205928. doi: 10.3389/fnins.2023.1205928
https://www.frontiersin.org/articles/10.3389/fnins.2023.1205928/full