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

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.; Preibisch, S.

doi:10.1101/343954

bioRxiv · 10.1101/343954

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

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.·Preibisch, S.

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Abstract

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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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., Preibisch, S.. 2018-06-10. BigStitcher: Reconstructing high-resolution image datasets of cleared and expanded samples. https://doi.org/10.1101/343954

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