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Learn2Reg

News!
  • Task 3 Continuation Update: Please see our Forum Post (and Updates)

Timeline:

  • 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
Learn2Reg Newsletter:

If you like to be added to the Learn2Reg newsletter, please fill out this form: https://forms.gle/2YKU1ykXovcAZSgV8


Learn2Reg Workshop:

L2R online:
  • 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)
Motivation:

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.
Learn2Reg removes pitfalls for learning and applying transformations by providing:
  • 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 learn2reg@gmail.com . See the full MICCAI Learn2Reg proposal  here

2022 Sponsors: