Interdisciplinary Institute for Neuroscience, University of Bordeaux, CNRS, Bordeaux, France.
Frankfurt Institute for Advanced Studies, Frankfurt, Germany.
Nat Methods. 2024 Oct;21(10):1884-1894. doi: 10.1038/s41592-024-02404-5. Epub 2024 Sep 18.
Quantitative microscopy workflows have evolved dramatically over the past years, progressively becoming more complex with the emergence of deep learning. Long-standing challenges such as three-dimensional segmentation of complex microscopy data can finally be addressed, and new imaging modalities are breaking records in both resolution and acquisition speed, generating gigabytes if not terabytes of data per day. With this shift in bioimage workflows comes an increasing need for efficient orchestration and data management, necessitating multitool interoperability and the ability to span dedicated computing resources. However, existing solutions are still limited in their flexibility and scalability and are usually restricted to offline analysis. Here we introduce Arkitekt, an open-source middleman between users and bioimage apps that enables complex quantitative microscopy workflows in real time. It allows the orchestration of popular bioimage software locally or remotely in a reliable and efficient manner. It includes visualization and analysis modules, but also mechanisms to execute source code and pilot acquisition software, making 'smart microscopy' a reality.
定量显微镜工作流程在过去几年中发生了巨大的变化，随着深度学习的出现，工作流程变得越来越复杂。长期存在的挑战，如复杂显微镜数据的三维分割，终于可以得到解决，新的成像方式在分辨率和采集速度方面都创下了纪录，每天生成数十千兆字节甚至数太字节的数据。随着生物图像工作流程的转变，对高效的编排和数据管理的需求也在不断增加，这需要多工具的互操作性和跨越专用计算资源的能力。然而，现有的解决方案在灵活性和可扩展性方面仍然受到限制，通常仅限于离线分析。在这里，我们介绍 Arkitekt，它是用户和生物图像应用程序之间的开源中间人，它可以实时实现复杂的定量显微镜工作流程。它允许以可靠和高效的方式在本地或远程编排流行的生物图像软件。它包括可视化和分析模块，但也有执行源代码和引导采集软件的机制，使“智能显微镜”成为现实。
