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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.
Keywords:Brain, Computer-Assisted Image Processing, Fluorescence Microscopy, Green Fluorescent Proteins, Software, Three-Dimensional Imaging, Animals, Caenorhabditis elegans, Drosophila, Mice
Source:Nature Methods
Publisher:Nature Publishing Group
Page Range:870-874
Date:September 2019
Additional Information:Copyright © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.
Official Publication:https://doi.org/10.1038/s41592-019-0501-0
External Fulltext:View full text on external repository or document server
PubMed:View item in PubMed
Related to:
https://edoc.mdc-berlin.de/17679/Preprint version

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