- Task 3 Continuation Update: Please see our Forum Post (and Updates)
- October 28th: Algorithm Sanity Check Submission (Upload-Link)
- November 9th: Q&A / sanity check feedback meeting
November 28th: Final submission of algorithms
Recordings and Slides
- Slides of the Learn2Reg Workshop: Task 3 Overview (PDF)and Challenge Overview (PDF)
- Recoding of the Tutorial at WBIR 2022 https://cloud.imi.uni-luebeck.de/s/sbxbD9F3k29C4Gz
- Recoding of the Learn2Reg presentation at WBIR 2022: https://cloud.imi.uni-luebeck.de/s/pYDEwbjxZNCYCNd
If you like to be added to the Learn2Reg newsletter, please fill out this form: https://forms.gle/2YKU1ykXovcAZSgV8
Challenge Results Zoom Meeting on Sunday, 18. September 14.00 CET
- Please consider giving some feedback: https://forms.gle/yvZ64vva4zhvzNpQA
- learn2reg.grand-challenge.org (This page) provides all information about the current Learn2Reg-Challenge, as well as our support forum and challenge archives
- github.com/MDL-UzL/L2R provides code for evaluation, zero-deformation fields for sanity-checks and other useful utilities for developing your image registration algorithm.
- learn2reg-test.grand-challenge.org provides benchmarking your registration algorithm with our secret test data. (Will be open to public after L2R 2022 ends at MICCAI)
Standardised benchmark for the best conventional and learning based medical registration methods:
- Analyse accuracy, robustness and speed on complementary tasks for clinical impact.
- Remove entry barriers for new teams with expertise in deep learning but not necessarily registration.
- python evaluation code for voxel displacement fields and open-source code all evaluation metrics
- anatomical segmentation labels, manual landmarks, masks and keypoint correspondences for deep learning
Learn2Reg addresses four of the imminent challenges of medical image registration:
- learning from relatively small datasets
- estimating large deformations
- dealing with multi-modal scans
- learning from noisy annotations
Evaluation: Comprehensive and fair evaluation criteria that include:
- Dice / surface distance and TRE toe measure accuracy and robustness of transferring anatomical annotations
- standard deviation and extreme values of Jacobian determinant to promote plausible deformations,
- low computation time for easier clinical translation evaluated using docker containers on GPUs provided by organisers.
Organisers / Contact: The full list of organisers can be found in the proposal document below, for any practical questions please contact Alessa Hering, Lasse Hansen, Christoph Großbröhmer and Mattias Heinrich at firstname.lastname@example.org . See the full MICCAI Learn2Reg proposal here