School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA.
Biodepot LLC, Seattle, 98195, WA, USA.
Sci Rep. 2022 Sep 2;12(1):14920. doi: 10.1038/s41598-022-19173-w.
Modern biomedical image analyses workflows contain multiple computational processing tasks giving rise to problems in reproducibility. In addition, image datasets can span both spatial and temporal dimensions, with additional channels for fluorescence and other data, resulting in datasets that are too large to be processed locally on a laptop. For omics analyses, software containers have been shown to enhance reproducibility, facilitate installation and provide access to scalable computational resources on the cloud. However, most image analyses contain steps that are graphical and interactive, features that are not supported by most omics execution engines. We present the containerized and cloud-enabled Biodepot-workflow-builder platform that supports graphics from software containers and has been extended for image analyses. We demonstrate the potential of our modular approach with multi-step workflows that incorporate the popular and open-source Fiji suite for image processing. One of our examples integrates fully interactive ImageJ macros with Jupyter notebooks. Our second example illustrates how the complicated cloud setup of an computationally intensive process such as stitching 3D digital pathology datasets using BigStitcher can be automated and simplified. In both examples, users can leverage a form-based graphical interface to execute multi-step workflows with a single click, using the provided sample data and preset input parameters. Alternatively, users can interactively modify the image processing steps in the workflow, apply the workflows to their own data, change the input parameters and macros. By providing interactive graphics support to software containers, our modular platform supports reproducible image analysis workflows, simplified access to cloud resources for analysis of large datasets, and integration across different applications such as Jupyter.
现代生物医学影像分析工作流程包含多个计算处理任务，导致可重复性问题。此外，影像数据集可以跨越空间和时间维度，具有荧光和其他数据的附加通道，导致数据集太大，无法在笔记本电脑上进行本地处理。对于组学分析，软件容器已被证明可以增强可重复性，方便安装，并提供对云的可扩展计算资源的访问。然而，大多数影像分析包含图形和交互步骤，这是大多数组学执行引擎不支持的功能。我们提出了支持软件容器图形的容器化和云启用的 Biodepot-workflow-builder 平台，并对其进行了扩展以支持影像分析。我们通过包含流行的开源 Fiji 套件进行图像处理的多步骤工作流程展示了我们模块化方法的潜力。我们的一个示例将完全交互式的 ImageJ 宏与 Jupyter 笔记本集成在一起。我们的第二个示例说明了如何自动化和简化使用 BigStitcher 拼接 3D 数字病理学数据集等计算密集型过程的复杂云设置。在这两个示例中，用户可以使用基于表单的图形界面，使用提供的示例数据和预设输入参数，通过单次点击执行多步骤工作流程。或者，用户可以交互式地修改工作流程中的图像处理步骤，将工作流程应用于自己的数据，更改输入参数和宏。通过为软件容器提供交互式图形支持，我们的模块化平台支持可重复的影像分析工作流程，简化了对大型数据集分析的云资源的访问，并且可以跨不同应用程序（如 Jupyter）进行集成。
