Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges

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.

Institutions

Publication Details

Journal
The Journal of Machine Learning for Biomedical Imaging
Published
2025-12-21
DOI
https://doi.org/10.59275/j.melba.2025-gc8c
Primary Topic
Medical Image Segmentation Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

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article

Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges

The Journal of Machine Learning for Biomedical Imaging
Medical Image Segmentation Techniques
article

Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges

article en

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.

The Journal of Machine Learning for Biomedical ImagingVol. 3(December 2025)
University of Geneva (CH), Uppsala University (SE), Brigham and Women's Hospital (US), Centre National de la Recherche Scientifique (FR), Nvidia (United Kingdom) (GB), Canon (Japan) (JP), LAC+USC Medical Center (US), Johns Hopkins University (US), Radboud University Nijmegen (NL), University of Electronic Science and Technology of China (CN), Inserm (FR), Linyi University (CN), Universität Hamburg (DE), University of California, Los Angeles (US), Tianjin University (CN), HES-SO University of Applied Sciences and Arts Western Switzerland (CH), Shenzhen University (CN), Vanderbilt University (US), Hong Kong University of Science and Technology (HK), Cornell University (US), University Hospital Augsburg (DE), TUM Klinikum (DE), University Medical Center (US), Radboud University Medical Center (NL), University of Agriculture in Krakow (PL), Los Angeles Medical Center (US), University Medical Center Hamburg-Eppendorf (DE), University Hospital and Clinics (US), Lübeck Academy of Music (DE), Centre for Artificial Intelligence and Robotics (IN), Hochschule Aalen (DE), Computational Physics (United States) (US), National Institute of Quality (PE), Krakow Cardiovascular Research Institute (PL), University Hospitals Geneva Medical Center (US), AGH University of Krakow (PL), University of Birmingham (GB), University of Hong Kong (HK), University of Lübeck (DE), Nantes Université (FR), Delft University of Technology (NL), Aalto University (FI)
Francis Crick Institute
Openalex Percentile: Top 57%
Medical Image Segmentation Techniques
DOIOpen Access PDFOpenAlex
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