Image processing tools for petabyte-scale light sheet microscopy data

IF 36.1 1区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Xiongtao Ruan, Matthew Mueller, Gaoxiang Liu, Frederik Görlitz, Tian-Ming Fu, Daniel E. Milkie, Joshua L. Lillvis, Alexander Kuhn, Johnny Gan Chong, Jason Li Hong, Chu Yi Aaron Herr, Wilmene Hercule, Marc Nienhaus, Alison N. Killilea, Eric Betzig, Srigokul Upadhyayula
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Abstract

Light sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are optimized for memory and performance. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson–Lucy deconvolution and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments. PetaKit5D offers versatile processing workflows for light sheet microscopy data including performant image input/output, geometric transformations, deconvolution and stitching. The software is efficient and scalable to petabyte-size datasets.

Abstract Image

百万亿字节级光片显微镜数据的图像处理工具。
光片显微镜是对亚细胞动态和大型生物标本进行高速三维成像的强大技术。然而,单次实验所产生的数据集往往从数百千兆字节到数百兆字节不等。传统的计算工具处理这些图像的速度远远慢于获取图像的时间,而且经常由于内存限制而完全失败。为了应对这些挑战,我们推出了 PetaKit5D,这是一种可扩展的软件解决方案,用于高效处理 PB 级光片图像。该软件集成了一套常用的处理工具,并对内存和性能进行了优化。显著的进步包括快速图像读写器、快速且内存效率高的几何变换、高性能理查森-卢西去卷积和可扩展的基于 Zarr 的拼接。这些功能比最先进的方法高出一个数量级以上,能够以现代成像相机的全太象素速率处理 PB 级图像数据。该软件通过大规模成像实验为生物发现开辟了新途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nature Methods
Nature Methods 生物-生化研究方法
CiteScore
58.70
自引率
1.70%
发文量
326
审稿时长
1 months
期刊介绍: Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.
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