Members from the IDEAS team will be lecturing and providing a hands-on project at the UCL Medical Image Computing Summer School (MedICSS). taking place from 19-23 July 2021. Now in its seventh year, the UCL Medical Image Computing Summer School allows young imaging researchers to explore a wide range of imaging analysis techniques.
The interactive 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 can change the results of FastSurfer compared to the original images.
For more information on the project, please check out the project site.
