Learn2Reg 2021





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.

Scope: The second edition Learn2reg challenge provides pre-preprocessed data (resample, crop, pre-align, etc.) for

  1. Intra-patient multimodal abdominal MRI and CT registration (122 scans in total, part of them unpaired) for diagnostic and follow-up.
  2. Intra-patient large deformation lung CT registration (20 training pairs, 10 test pairs, all inspiration / expiration) for lung ventilation estimation.
  3. Inter-patient large scale brain MRI registration (>400 unpaired training scans, ~100 test scans) for shape analysis.

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 Adrian Dalca, Alessa Hering, Lasse Hansen and Mattias Heinrich at learn2reg@gmail.com . See the full MICCAI Learn2Reg proposal  here