Atlas registration · Sample space
Comparing imaging data across a cohort usually requires mapping specimens to a common anatomical framework. Preclinical neuroscience labs in mouse, rat, and zebrafish studies choose among several atlas alignment strategies — each suited to different specimen types and scientific questions.
Atlas alignment historically means registering the specimen into a fixed reference coordinate frame. Open-source pipelines — BrainGlobe and related toolkits, ANTs, or custom Python workflows — and commercial atlas modules in platforms such as Imaris or Aivia warp the sample toward Allen Common Coordinate Framework version 3 (Allen CCFv3) or other references in standard space. That approach is established and widely used, especially for in vivo MRI or ex vivo brain-only volumes. Registering the specimen into atlas standard space is limiting for two reasons: it does not preserve native sample-space geometry and can introduce warping artifacts. Furthermore, most available reference atlases are built from brains imaged with techniques that require removing the brain from the skull — so aligning to atlas standard space cannot accommodate structures from in situ studies, such as cranial bone or vessels, in the same coordinate framework.
The NeuroSimplicity Imaging Suite maps reference atlases onto your specimen rather than warping your specimen into the atlas. Supported references include Allen Atlas CCFv3, Blue Brain Atlas, Z-Fish Atlas, and NeuroSimplicity Cranial and Vessel Atlases. When you add more than one sample of the same modality, the platform automatically generates a population atlas for that cohort. Where a reference atlas is available, you compare cohort results against it; you also compare samples between groups using their population atlases — for example, mutant versus control or treatment versus control. The platform extracts features, applies labels, and calculates quantitative metrics in native sample space — the approach published for cranial micro-computed tomography (micro-CT) in Cell Reports Methods (Rosenblum et al., 2021). When labs license the full Imaging Suite (Anatomic Imaging, Molecular Imaging, Digital Pathology, and Spatial Omics Modules together), they can integrate light-sheet, histology, whole-slide imaging, and other modalities from the same cohort into one native sample-space framework — with atlas labels and quantitative metrics applied across all of them.
Atlas alignment to native sample space is part of the iterative in situ micro-CT workflow documented in Cell Reports Methods (2021) and STAR Protocols (2023). You can explore step-by-step protocol guides derived from those papers — linked in Next steps below.
For cleared-tissue light-sheet, whole-slide histology, and other modalities, registering the atlas to the sample enables regional comparison without exporting volumes to a separate alignment pipeline.
This workflow is documented in Cell Reports Methods and STAR Protocols, and applied in Nature discovery studies by NIH NINDS collaborators. Full citations, figures, and paper links are on our publications page.
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