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
- Intra-patient multimodal abdominal MRI and CT registration (122 scans in total, part of them unpaired) for diagnostic and follow-up.
- Intra-patient large deformation lung CT registration (20 training pairs, 10 test pairs, all inspiration / expiration) for lung ventilation estimation.
- 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 firstname.lastname@example.org . See the full MICCAI Learn2Reg proposal here