新基准数据集推动医学影像配准技术进步
Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
- 新增多模态、脑部跨被试无监督配准等三类任务
- 涵盖显微镜图像，首次实现微观尺度基准评估
- 激发新方法如金字塔特征与关键点对齐
医学图像配准对临床应用至关重要，公平的基准测试是监测领域进展的关键。自2020至2023年，Learn2Reg挑战赛已发布多个互补数据集并建立评估指标。2024年版本拓展了挑战范围，覆盖更广的配准场景，特别是在模态多样性和任务复杂性方面，新增三大任务：大规模多模态配准、无监督跨被试脑部配准，以及Learn2Reg中首个聚焦显微镜图像的基准。新数据集还催生了新方法的发展，包括可逆性约束、金字塔特征、关键点对齐和实例优化。访问 Learn2Reg 官网：https://learn2reg.grand-challenge.org。
原文摘要 · Abstract (English)
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. Visit Learn2Reg at https://learn2reg.grand-challenge.org.
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