Lee Tirrell - Reproducible Exploration of Neuroimaging Data | JupyterCon 2020

Brief Summary Artificial intelligence (AI) algorithms enhance the ability to quantitatively assess medical imaging data. However, quality control and data reproducibility are essential steps to ensure consistency and high quality results. Jupyter provides a platform to interactively visualize and assess brain MRI data alongside the AI-derived measurements useful for clinical insights in a reproducible manner. Outline Overview Our proposal outlines the use of Jupyter as an interactive platform for visualizing and quality checking brain MRI data in a reproducible manner. While this project is focused on neuroimaging, many of the tools and techniques we describe have broad application in other domains. We will show how we manage data and track outputs, interact with the tabular results of neuroimaging processing algorithms, and inspect 3D images, all from the same notebook. Reproducibility is essential for bridging the gap between research labs and real world settings. Complex patterns in medical images are summarized by AI algorithms into a small number of features to derive clinical insights. Reliable and accurate results are necessary to gain trust in automated procedures, as opposed to more laborious manual inspection commonly used in practice. Environments that make it easy for for developers to follow best practices will help create tools that can more rapidly deploy cutting edge technology. For reproducible data management, we use Quilt, a platform and Python library built on top of Amazon Web Services' S3 data storage system to create and track versioned datasets. Quilt ensures that results can be correctly matched to the input data and processing stream used to create them, regardless of any changes that happened since they were created. The neuroimaging processing software we use is based on the FreeSurfer analysis suite. Results consist of a 3D segmentation image, where the brain is labeled as various anatomical structures, as well as tabular data contai