Learn2Reg - The Challenge [article]

Adrian Dalca, Yipeng Hu, Tom Vercauteren, Mattias Heinrich, Lasse Hansen, Marc Modat, Bob De Vos, Yiming Xiao, Hassan Rivaz, Matthieu Chabanas, Ingerid Reinertsen, Bennett Landman (+4 others)
2020 Zenodo  
This is the challenge design document for "Learn2Reg - The Challenge", accepted for MICCAI 2020. Medical image registration plays a very important role in improving clinical workflows, computer-assisted interventions and diagnosis as well as for research studies involving e.g. morphological analysis. Besides ongoing research into new concepts for optimisation, similarity metrics and deformation models, deep learning for medical registration is currently starting to show promising advances that
more » ... sing advances that could improve the robustness, computation speed and accuracy of conventional algorithms to enable better practical translation. Nevertheless, there exists no commonly used benchmark dataset to compare state-of-the-art learning based registration among another and with their conventional (not trained) counterparts. With few exceptions (CuRIOUS at MICCAI 2018/2019 and the Continuous Registration Challenge at WBIR 2018) there has also been no comprehensive registration challenge covering different anatomical structures and evaluation metrics. We also believe that the entry barrier for new teams to contribute to this emerging field are higher than e.g. for segmentation, where standardised datasets (e.g. Medical Decathlon, BraTS) are easily available. In contrast, many registration tasks, require resampling from different voxel spacings, affine pre-registration and can lead to ambiguous and error-prone evaluation of whole deformation fields. We propose a simplified challenge design that removes many of the common pitfalls for learning and applying transformations. We will provide pre-preprocessed data (resample, crop, pre-align, etc.) that can be directly employed by most conventional and learning frameworks. Only displacement fields in voxel dimensions in a standard orientation will have to be provided by participants and python code to test their application to training data will be provided as open-source along with all evaluation metrics. Our challenge will consist of 4 clinically relevant sub-tasks (datasets) that are [...]
doi:10.5281/zenodo.3715651 fatcat:ibth5xalsfenbhgzvt6kryfjh4