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

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Item Type:	Article
Title:	BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples
Creators Name:	Hörl, D., Rojas Rusak, F., Preusser, F., Tillberg, P., Randel, N., Chhetri, R.K., Cardona, A., Keller, P.J., Harz, H., Leonhardt, H., 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
ISSN:	1548-7091
Publisher:	Nature Publishing Group
Volume:	16
Number:	9
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
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