Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
| Type of publication: | Article |
| Citation: | |
| Publication status: | Published |
| Journal: | Hansen et al. 2025 / Melba Journal - Machine Learning for Biomedical Imaging |
| Volume: | Volume 3 |
| Year: | 2025 |
| Month: | December |
| DOI: | https://doi.org/10.59275/j.melba.2025-gc8c |
| Abstract: | Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress in the field. To date, the Learn2Reg 2020-2023 challenges have released several complementary datasets and established metrics for evaluations. Building on this foundation, the 2024 edition expands the challenge’s scope to cover a wider range of registration scenarios, particularly in terms of modality diversity and task complexity, by introducing three new tasks, including large-scale multi-modal registration and unsupervised inter-subject brain registration, as well as the first microscopy-focused benchmark within Learn2Reg. The new datasets also inspired new method developments, including invertibility constraints, pyramid features, keypoints alignment and instance optimisation. |
| Keywords: | Data Challenges · (Bio-) Medical Image Registration |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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