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

Item Type:Article
Title:BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples
Creators Name:Hörl, D. and Rojas Rusak, F. and Preusser, F. and Tillberg, P. and Randel, N. and Chhetri, R.K. and Cardona, A. and Keller, P.J. and Harz, H. and Leonhardt, H. and Treier, M. and Preibisch, S.
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.
Source:Nature Methods
ISSN:1548-7091
Publisher:Nature Publishing Group (U.S.A.)
Date:5 August 2019
Official Publication:https://doi.org/10.1038/s41592-019-0501-0
PubMed:View item in PubMed
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https://edoc.mdc-berlin.de/17679/Preprint version

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