%Aigaion2 BibTeX export von HES SO Valais Publications
%Monday 31 August 2026 07:30:16 AM

@ARTICLE{,
    author = {Hansen, Lasse and Heyer, Wiebke and Grossbr{\"{o}}hmer, Christoph and Madesta, Frederic and Sentker, Thilo and Jiazheng, Wang and Zhang, Yuxi and Zhang, Hang and Liu, Min and Wang, Junyi and Zhu, Xi and Li, Yuhua and Wang, Liwen and Morozov, Daniil and Houachine, Nazim and Honkamaa, Joel and Marttinen, Pekka and Zhou, Yichao and Tan, Zuopeng and Wang, Zhuoyuan and Wang, Yi and Zhou, Hongchao and Hu, Shunbo and Zhang, Yi and Tao, Qian and F{\"{o}}rner, Lukas and Wendler, Thomas and Jian, Bailiang and Wachinger, Christian and Kim, Jin and Ruan, Dan and Wodzinski, Marek and M{\"{u}}ller, Henning and Mok, Tony Chi Wing and Jia, Xi and Duan, Jinming and Brudfors, Mikael and Ahmadi, Seyed-Ahmad and Zhu, Yunzheng and Hsu, William and Kapur, Tina and Wells, William M. and Golby, Alexandra and Carass, Aaron and Bai, Harrison and Liu, Yihao and Paul-Gilloteaux, Perrine and Lindblad, Joakim and Sladoje, Natasa and Walter, Andreas and Chen, Junyu and Dorent, Reuben and Hering, Alessa and Heinrich, Mattias},
  keywords = {Data Challenges {\textperiodcentered} (Bio-) Medical Image Registration},
     month = dec,
     title = {Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges},
   journal = {Hansen et al. 2025 / Melba Journal - Machine Learning for Biomedical Imaging},
    volume = {Volume 3},
      year = {2025},
       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.}
}