Implementing Reproducible Medical Image Analysis Pipelines

Leaders: Dave Cash, Haroon Chughtai

This project provides a demonstration of how to implement a reproducible medical image analysis pipeline for scalable, high throughput analysis without the need for substantial experience of coding. It will also show how solutions for maintaining the privacy of study participants can be implemented with low overhead. It will use all open source software, in particular the data and analysis will be managed through XNAT, a widely used web-based platform. In this tutorial, we will go through the benefits this platform for automating handling, importing, and cleaning of DICOM data, conversion to Nifti, de-facing structural T1 data to provide additional assurance of privacy, and finally volumetric and cortical thickness analysis using FastSurfer, a free deep learning implementation of FreeSurfer. The project will determine how much de-facing algorithms change the results of FastSurfer compared to the original images.

Further courses and tutorials on medical image data management, software development, and medical image analysis will soon be available at Health and Bioscience IDEAS, a UKRI-funded training program around medical imaging for UK researchers.