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.

Biomedical image analyses can require many steps processing different types of data. Analysis of increasingly large data sets often exceeds the capacity of local computational resources. We present an easy-to-use and modular cloud platform that allows biomedical researchers to reproducibly execute and share complex analytical workflows to process large image datasets. The workflows and the platform are encapsulated in software containers to ensure reproducibility and facilitate installation of even the most complicated workflows. The platform is both graphical and interactive allowing users to use the viewer of their choice to adjust the image pre-processing and analysis steps to iteratively improve the final results. We demonstrate the utility of our platform via two use cases in focal adhesion and 3D imaging analyses. In particular, our focal adhesion workflow demonstrates integration of Fiji with Jupyter Notebooks. Our 3D imaging use case applies Fiji/BigStitcher to big datasets on the cloud. The accessibility and modularity of the cloud platform democratizes the application and development of complex image analysis workflows.

Graphical processing units can greatly accelerate image processing but adoption has been hampered by the need for specialized hardware and software. The cloud offers inexpensive on-demand instances that can be pre-configured with the necessary software. Specifically, we use the Biodepot-workflow-builder (Bwb) to deploy a containerized version of Fiji that includes the CLIJ package to use GPUs on the cloud. In addition, we provide an Amazon Machine Image (AMI) with the correct drivers and Docker images pre-loaded. We demonstrate the portability and reproducibility of the platform by deploying an interactive Fiji/CLIJ workflow on both Amazon Web Services and IBM cloud. The workflows produce identical results while providing a 29-fold reduction in execution time.