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Nat Methods. 2019 Sep;16(9):870-874. doi: 10.1038/s41592-019-0501-0. Epub 2019 Aug 5.

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

Author information

1
Department of Biology II, Ludwig-Maximilians-Universität München, Munich, Germany.
2
Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
3
Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
4
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
5
Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK.
6
Charité-Universitätsmedizin Berlin, Berlin, Germany.
7
Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany. stephan.preibisch@mdc-berlin.de.

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.

PMID:
31384047
DOI:
10.1038/s41592-019-0501-0

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