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BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples

Authors :
Fabio Rojas Rusak
Friedrich Preusser
Stephan Preibisch
Hartmann Harz
Mathias Treier
Heinrich Leonhardt
Nadine Randel
Raghav K. Chhetri
Paul W. Tillberg
David Hörl
Albert Cardona
Philipp J. Keller
Source :
Nature Methods. 16:870-874
Publication Year :
2019
Publisher :
Springer Science and Business Media LLC, 2019.

Abstract

Light-sheet imaging of cleared and expanded samples creates terabyte-sized datasets that consist of many unaligned three-dimensional image tiles, which must be reconstructed before analysis. We developed the BigStitcher software to address this challenge. BigStitcher enables interactive visualization, fast and precise alignment, spatially resolved quality estimation, real-time fusion and deconvolution of dual-illumination, multitile, multiview datasets. The software also compensates for optical effects, thereby improving accuracy and enabling subsequent biological analysis.

Details

ISSN :
15487105 and 15487091
Volume :
16
Database :
OpenAIRE
Journal :
Nature Methods
Accession number :
edsair.doi...........182e28ed2fc7474724e35f2916ea5e80
Full Text :
https://doi.org/10.1038/s41592-019-0501-0