BigStitcher：重建已清除和扩展样本的高分辨率图像数据集。

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

机构信息

Department of Biology II, Ludwig-Maximilians-Universität München, Munich, Germany.

Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.

出版信息

Nat Methods. 2019 Sep;16(9):870-874. doi: 10.1038/s41592-019-0501-0. Epub 2019 Aug 5.

DOI:10.1038/s41592-019-0501-0

PMID:31384047

Abstract

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.

摘要

利用光片成像技术对经过透明化和扩展处理的样本进行成像，会产生包含大量未对齐的三维图像块的兆兆字节级数据集，在进行分析之前，这些数据集必须进行重建。我们开发了 BigStitcher 软件来应对这一挑战。BigStitcher 支持交互式可视化、快速而精确的对齐、空间分辨质量评估、实时融合和双激发、多视场、多图块数据集的反卷积。该软件还可以补偿光学效应，从而提高准确性，并为后续的生物学分析提供支持。