SeriesZoom

SeriesZoom is one of several tools developed by the Nesys laboratory, University of Oslo and participating to EBRAINS with the aim of facilitating brain atlas based analysis and integration of experimental data and knowledge about the human and rodent brain. SeriesZoom is an online viewer, typically used for high resolution histological images. Building on the open standard Deep Zoom Image (DZI) format, it is able to efficiently visualise very large brain images in the gigapixel range, allowing to zoom from common, display-sized overview resolutions down to the microscopic resolution without downloading the underlying very large image dataset.

Launch online
Contributors
GC

Other Tools

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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

The EBRAINS Collaboratory offers researchers and developers a secure environment to work with others. You control the level of collaboration by sharing your projects with specific users, teams or all of the Internet. Many researchers are sharing their work already; several services, tools, datasets and other resources are publicly available, and many more are available for registered users.

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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