@article {65079,
	title = {BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples.},
	journal = {Nature Methods},
	volume = {16},
	year = {2019},
	month = {2019 Sep},
	pages = {870-74},
	abstract = {<p>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.</p>
},
	issn = {1548-7091},
	doi = {10.1038/s41592-019-0501-0},
	url = {http://www.nature.com/articles/s41592-019-0501-0http://www.nature.com/articles/s41592-019-0501-0.pdfhttp://www.nature.com/articles/s41592-019-0501-0.pdfhttp://www.nature.com/articles/s41592-019-0501-0},
	author = {H{\"o}rl, David and Rojas Rusak, Fabio and Preusser, Friedrich and Tillberg, Paul and Randel, Nadine and Chhetri, Raghav K. and Cardona, Albert and Keller, Philipp J. and Harz, Hartmann and Leonhardt, Heinrich and Treier, Mathias and Preibisch, Stephan}
}
