Technical Note: STRATIS: A Cloud-enabled Software Toolbox for Radiotherapy and Imaging Analysis

Purpose Recent advances in computational resources, including software libraries and hardware, have enabled the use of high-dimensional, multi-modal datasets to build Artificial Intelligence (AI) models and workflows for radiation therapy and image analysis. The purpose of Software Toolbox for RAdioTherapy and Imaging analysiS (STRATIS) is to provide cloud-enabled, easy-to-share software workflows to train and deploy AI models for transparency and multi-institutional collaboration. Method STRATIS leverages open source medical image informatics software for application-specific analysis. Jupyter notebooks for AI modeling workflows are provided with Python language as the base kernel. In addition to Python, workflows use software written in other languages, such as MATLAB, GNU-Octave, R, and C++, with the help of bridge libraries. The workflows can be run on a cloud platform, local workstation, or an institutional HPC cluster. Computational environments are provided in the form of publicly available docker images -and build scripts for local Anaconda environments. Utilities provided with STRATIS simplify bookkeeping of associations between imaging objects and allow chaining data processing operations defined via a setting file for AI models. Results Workflows available on STRATIS can be broadly categorized into image segmentation, deformable image registration, and outcomes modeling for radiotherapy toxicity and tumor control using radiomics and dosimetry features. The STRATIS-forge GitHub organization https://www.github.com/stratis-forge hosts build-scripts for Docker and Anaconda as well as Jupyter notebooks for analysis workflows. The software for building environments and workflow notebooks has open source-GNU-GPL copyright, and AI models retain the copyright chosen by their original developers. Conclusion STRATIS enables researchers to deploy and share AI modeling workflows for radiotherapy and image analysis. STRATIS is publicly available on Terra.bio’s FireCloud platform with a pre-deployed computational environment and on GitHub organization for users pursuing local deployment.