BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples.
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Hörl, David https://orcid.org/0000-0003-1710-1708
Rojas Rusak, Fabio https://orcid.org/0000-0002-0637-9467
Preusser, Friedrich https://orcid.org/0000-0001-8231-2195
Tillberg, Paul https://orcid.org/0000-0002-2568-2365
Randel, Nadine https://orcid.org/0000-0002-7817-4137
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.
Description
Keywords
Animals, Brain, Caenorhabditis elegans, Drosophila, Female, Green Fluorescent Proteins, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Mice, Microscopy, Fluorescence, Software
Journal Title
Nat Methods
Conference Name
Journal ISSN
1548-7091
1548-7105
1548-7105
Volume Title
16
Publisher
Springer Science and Business Media LLC
Publisher DOI
Rights
All rights reserved