Constructing and optimizing 3D atlases from 2D data with application to the developing mouse brain.
IF: 8.713
Cited by: 4


3D imaging data necessitate 3D reference atlases for accurate quantitative interpretation. Existing computational methods to generate 3D atlases from 2D-derived atlases result in extensive artifacts, while manual curation approaches are labor-intensive. We present a computational approach for 3D atlas construction that substantially reduces artifacts by identifying anatomical boundaries in the underlying imaging data and using these to guide 3D transformation. Anatomical boundaries also allow extension of atlases to complete edge regions. Applying these methods to the eight developmental stages in the Allen Developing Mouse Brain Atlas (ADMBA) led to more comprehensive and accurate atlases. We generated imaging data from 15 whole mouse brains to validate atlas performance and observed qualitative and quantitative improvement (37% greater alignment between atlas and anatomical boundaries). We provide the pipeline as the MagellanMapper software and the eight 3D reconstructed ADMBA atlases. These resources facilitate whole-organ quantitative analysis between samples and across development.


3D atlas
computational biology
developmental biology
image processing
mouse development
systems biology
tissue clearing


Young, David M
Fazel Darbandi, Siavash
Schwartz, Grace
Bonzell, Zachary
Yuruk, Deniz
Nojima, Mai
Gole, Laurent C
Rubenstein, John Lr
Yu, Weimiao
Sanders, Stephan J

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