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

By David Hörl, Fabio Rojas Rusak, Friedrich Preusser, Paul W. Tillberg, Nadine Randel, R. Chhetri, Albert Cardona, Philipp J. Keller, H. Harz, H. Leonhardt and 2 more. Nature Methods, 2018

305 citations recorded by the source corpus.

Subfields: Biophysics, Media Technology

View Paper: https://www.semanticscholar.org/paper/1997b7e06612d0213dd150eff70e98216e83edab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Curtis T. Rueden
2. Kevin W. Eliceiri
3. Florian Jug
4. Pavel Tomancak
5. Ellen T. A. Dobson
6. Alexandra B. Schroeder
7. Robert Haase
8. Deborah Schmidt

In Media Technology

1. Curtis T. Rueden
2. Florian Jug
3. Kevin W. Eliceiri

Citations in the corpus

• The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis (2020, methodology): “31 BigStitcher supports file sizes that can be as large as many terabytes of data, such as those often produced during light sheet microscopy acquisition.”
• Labkit: Labeling and Segmentation Toolkit for Big Image Data (2021, methodology): “The dataset was then saved in the BigDataViewer XML+HDF5 format using BigStitcher (Hörl et al., 2019).”
• Tissue clearing and its applications in neuroscience (2020, methodology): “Thus, a typical workaround in existing approaches to high-resolution imaging of large samples, such as lattice light-sheet microscopy or IsoView light-sheet microscopy, is to rely on tiling strategies for imaging specimens as large as entire cleared and/or expanded nervous systems 117,118 .”