Constructing and optimizing 3D atlases from 2D data with application to the developing mouse brain release_bwzrphk2fjddjhjspp7ran4gw4

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

Published in eLife by eLife Sciences Publications, Ltd.

2021   Volume 10

Abstract

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 fifteen 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.
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Type  article-journal
Stage   published
Date   2021-02-11
Language   en ?
DOI  10.7554/elife.61408
PubMed  33570495
PMC  PMC7994002
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