Apr 9, 2020

Automated Edge-Aware Refinement of Anatomical Atlases in 3D and Its Application to the Developing Mouse Brain

BioRxiv : the Preprint Server for Biology
D. M. YoungL. Gole

Abstract

Anatomical atlases are valuable resources for investigating organogenesis, anatomy, pathology, and therapeutic targeting. Increasing utilization of 3D microscopy from intact tissues necessitates corresponding 3D atlases and anatomical labels. Generating these atlases requires either labor-intensive manual curation or transformation of existing atlases from physically sectioned tissues, resulting in artifacts. Here, we present an automated, edge-aware method to transform existing atlases including imputing incomplete labels and reducing artifacts whilst maintaining anatomical boundaries. Applying these techniques to the Allen Developing Mouse Brain Atlas (ADMBA), we demonstrate improvements, both qualitatively and quantitatively. To test the refined 3D atlas, we generated whole-brain nuclei-resolution images from 15 mice at postnatal day 0, identifying 35 million nuclei per brain. Our atlas improved the alignment between label and anatomical edges by 37% (p.adj=0.007). To promote large-scale comparative analysis of cytoarchitecture, we provide these methods as the MagellanMapper software package and the refined ADMBA atlas as a resource.

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Mentioned in this Paper

SLPI protein, human
Study
Myocardial Perfusion Imaging
House mice
Bio-Informatics
Mexican
TAPBP protein, human
Genomics
Massively-Parallel Sequencing
Muscle

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