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AirLab - MICCAI Tutorial 2019

Date: October 17, 2019 (afternoon)
Room: Madrid 2

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Preparation for Hands-on Session:
If you bring with you your laptop, in the second session you will have the opportunity to try out AirLab with our assistance. As the internet at conferences are usually slow we highly recommend to install PyTorch 1.1 (and CUDA if you have a GPU in your laptop) prior to the tutorial.

Autograd Image Registration Laboratory

A myriad of medical image analysis tasks rely on image registration as a “preprocessing” on which the target model builds on. The development and implementation of such image registration algorithms have been tedious so far and often requires specific programming skills such as parallel computing with CUDA at a low abstraction level. In this tutorial, the novel image registration framework: Autograd Image Registration Laboratory (AirLab) will be introduced. AirLab is a modern framework for image registration which allows rapid-prototyping for image registration algorithms. It is written in Python and utilizes the autograd functionality of PyTorch in order to exempt the developer from providing explicit gradients. The hands-on tutorial will be for beginners and intermediate-level students, experienced researchers and industry delegates. Participants will learn the principles of AirLab, how AirLab can be integrated into a project and how AirLab can be used in their daily research.

Diffeomorphic Transformations



Session 1 (14:00):

Introduction to Medical Image Registration

Applied Image Registration

Introduction to AirLab

Coffee Break (15:30):

Session 2 (16:30):

Hello World with AirLab

The Future of AirLab


Christoph Jud

Dr. Christoph Jud Department of Biomedical Engineering, University of Basel, Switzerland

Robin Sandkühler

Robin Sandkühler, M.Sc. Department of Biomedical Engineering, University of Basel, Switzerland


Robin Sandkühler, Philippe C. Cattin, Christoph Jud



Robin Sandkuehler, Christoph Jud, Simon Andermatt, and Philippe C. Cattin. “AirLab: Autograd Image Registration Laboratory”. arXiv preprint arXiv:1806.09907, 2018. link