We are delighted to announce this years' Learn2Reg challenge, which comes with some innovations. These are this year's tasks:
- Task 1: CT Lung Registration (NLST) - Screening Data
- Task 2: Continuation of previous year’s tasks
- Task 3: Universal registration framework
Additionally, we are very happy to launch Learn2Reg-Test once our MICCAI workshop is completed! Over there, participants and non-participants are welcome to test their algorithms (which may be trained on our L2R training data) against our test set. With this benchmarking website, we hope to facilitate the reprocibility of image registration algorithms and want to promote fair, easy-to-use and neutral evaluation. Furthermore, it has the capacities to become a repository of registration methods, which can easily compared and employed.
Task1 : CT Lung Registration (NLST)
This year, we ask you to register longitudinal CT Lung Images. We provide a substantial amount of image data, as well as automatically generated lung masks and keypoints for deep learning supervision.
Training, Validation and Test Data
Task2: Continuation of previous year’s tasks
Although it may seem to be a bit boring, our second task of Learn2Reg 2022 ist the continuation of our previous tasks. We have 3 mayor reasons to do so:
- Thoughout the past years, we have released a lot of data for image registration tasks, including inter- and intrasubject, inter- and intramodal, MR, CT, US images, labels, masks and keypoints. We want this repository of registration data to be tackled with newly developed algorithms and are eager to see the improvement on these datasets.
- For ease-of -use, we have restructured our dataset into a unfying architecture. We hope to lower the barrier to train and evaluate on different datasets and thus to fairly compare which algorithm works best for which tasks. All prevoius tasks can be evaluated on our test data on Learn2Reg-Test, which will serve as a benchmarking and emloyment website for registration algorithms.
- If you have adapted the new dataset structure and developed (or adapted) your method, it is only a small step to participate in our most exciting task: The Universal Training Framework.
Task3: Universal registration framework
This year, we will feature the first (to our knowledege) Type 3 medical image registration challenge. We seek to find the registration framework, which works best on a verity of tasks. Therfore, participants are asked to provide their algorithms to us as docker containers, which will be trained on our hardware on hidden datasets. We hope that if you worked on Task 2 and adapted to the new dataset architecture, adpoting these methods to a rule-based universial framework will be relatively easy. Additionally, we will provide examplary algorithms and a template!