QUINT workflow

Extract, quantify and analyse features from rodent histological images

 

The QUINT workflow takes you through steps to quantify and analyse labelled features within a known atlas space.

  • Leverage a streamlined, tested workflow for analysing labelled features in large numbers of histological sections from mouse or rat
  • Obtain quantitative measures in atlas-defined regions
  • Customise the granularity of regions-of-interest to fit the needs of your analysis

The QUINT workflow comprises a suite of software designed to support atlas-based quantification. All the software have user interfaces, with no coding ability required. It generates object counts and percentage coverage per atlas-region, in addition to point clouds for visualising the features in 3D.

It currently supports quantification relative to the following atlases:

  • Allen Mouse Brain Atlas Common Coordinate Framework version 3 (2015 and 2017) (CCFv3)
  • Waxholm Atlas of the Sprague Dawley rat, version 2, 3 and 4 (WHS rat brain atlas).
QUINT Workflow

 

Analyse large amounts of histological image data from rat or mouse brains

Nutil
ilastik
QuickNII
VisuAlign
MeshView
QCAlign
 
 

Use QUINT for:

  • Cell count analysis
  • Analysis of tract-tracing connections
  • Resizing, rotating and renaming large Tiff images

Access a QUINT dataset on EBRAINS
Use Nutil to edit large Tiff images

 

Get involved in the QUINT community

Are you interested in atlas based spatial quantification? Discover the QUINT workflow and interact with us to customise the workflow for your needs: Count cells, AD plaques and quantify connectivity traces in 2D images from mice and rat. The QUINT workflow is highly flexible and has developed over time in response to users’ needs. We look forward to continuing this collaboration with the research community and welcome all requests, big and small. Post us questions directly through github or at https://ebrains.eu/support/
 

Github Nutil/ QUINT
Github QuickNII/VisuAlign
Github ilastik
Course Neuroscience data integration

QUINT community



 

Related Publications

See all publications
Publication

Nutil: A Pre- and Post-processing Toolbox for Histological Rodent Brain Section Images

Groeneboom N.E., Yates S.C., Puchades M.A. and Bjaalie J.G. (2020) Front. Neuroinform. 14:37
Publication

QUINT: Workflow for quantification and spatial analysis of features in histological images from rodent brain

Yates S.C., Groeneboom N.E., Coello C., Lichtenthaler S.F., Kuhn P.H., Demuth H.U., Hartlage-Rübsamen M., Roßner S., Leergaard T., Kreshuk A., Puchades M.A., Bjaalie JG. (2019) Front. Neuroinform. 13, 75
Publication

Spatial registration of serial microscopic brain images to three-dimensional reference atlases with the QuickNII tool

Puchades M.A., Csucs G., Lederberger D., Leergaard T.B., and Bjaalie J.G. (2019) PLosONE. 14 (5): e0216796
Publication

ilastik:interactive machine learning for (bio) image analysis

Berg S., Kutra D., Kroeger T., Straehle C.N., Kausler B.X., Haubold C., et al. (2019) Nat Methods. 16, 1226–1232.
Launch online

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siibra-explorer

siibra-explorer is built around an interactive 3D view of the brain displaying a unique selection of detailed templates and parcellation maps for the human, macaque, rat or mouse brain, including BigBrain as a microscopic resolution human brain model at its full resolution of 20 micrometres.

Collaboratory

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MeshView

MeshView is a web application for real-time 3D display of surface mesh data representing structural parcellations from volumetric atlases, such as the Waxholm Space Atlas of the Sprague Dawley Rat Brain. MeshView is available as an EBRAINS community app, accessible via a collab (further info: https://wiki.ebrains.eu/bin/view/Collabs/image-registration-and-analysis-demo/) Key features: orbiting view with toggleable opaque/transparent/hidden parcellation meshes rendering user defined cut surface as if meshes were solid objects rendering point-clouds (simple type-in, or loaded from a JSON format) coordinate system is compatible with QuickNII (https://www.nitrc.org/projects/quicknii)

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