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

By David Hörl, Fabio Rojas Rusak, Friedrich Preusser, Paul Tillberg, Nadine Randel, Raghav K. Chhetri, Albert Cardona, Philipp J. Keller, Hartmann Harz, Heinrich Leonhardt, Mathias Treier, Stephan Preibisch

Posted 10 Jun 2018
bioRxiv DOI: 10.1101/343954 (published DOI: 10.1038/s41592-019-0501-0)

New methods for clearing and expansion of biological objects create large, transparent samples that can be rapidly imaged using light-sheet microscopy. Resulting image acquisitions are terabytes in size and consist of many large, unaligned image tiles that suffer from optical distortions. We developed the BigStitcher software that efficiently handles and reconstructs large multi-tile, multi-view acquisitions compensating all major optical effects, thereby making single-cell resolved whole-organ datasets amenable to biological studies.

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