BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples
BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples
复制标题
DOI：
10.1038/s41592-019-0501-0
复制
发表时间：
2019-09-01
期刊：
影响因子：
48
通讯作者：
Preibisch, Stephan
中科院分区：
文献类型：
--
作者：
Hoerl, David;Rusak, Fabio Rojas;Preibisch, Stephan
对清除和扩大的样本进行光片成像会产生 TB 大小的数据集，这些数据集由许多未对齐的三维图像瓦片组成，必须在分析前对其进行重建。我们开发了 BigStitcher 软件来应对这一挑战。BigStitcher 可实现交互式可视化、快速精确的配准、空间分辨率质量评估、实时融合以及双照明、多瓣、多视图数据集的解卷积。该软件还能对光学效应进行补偿，从而提高精确度，实现后续生物分析。
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.