This report presents a generic image registration benchmark with automatic evaluation using landmark annotations. The key features of the BIRL framework are: easily extendable, performance evaluation, parallel experimentation, simple visualisations, experiment's time-out limit, resuming unfinished experiments. From the research practice, we identified and focused on these two main use-cases: (a) comparison of user's (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experimenting SOTA methods on user's custom dataset (which should contain landmark annotation). Moreover, we present an integration of several standard image registration methods aiming at biomedical imaging into the BIRL framework. This report also contains experimental results of these SOTA methods on the CIMA dataset, which is a dataset of Whole Slice Imaging (WSI) from histology/pathology containing several multi-stain tissue samples from three tissue kinds. Source and results: https://borda.github.io/BIRL

中文翻译：

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BIRL：具有地标验证的图像配准方法的基准

本报告介绍了使用地标注释自动评估的通用图像配准基准。BIRL 框架的主要特点是：易于扩展、性能评估、并行实验、简单的可视化、实验的超时限制、恢复未完成的实验。从研究实践中，我们确定并专注于这两个主要用例：（a）在通用数据集上比较用户（新开发的）方法与一些最先进的（SOTA）方法和（b）实验用户自定义数据集上的 SOTA 方法（应包含地标注释）。此外，我们将针对生物医学成像的几种标准图像配准方法集成到 BIRL 框架中。本报告还包含这些 SOTA 方法在 CIMA 数据集上的实验结果，这是来自组织学/病理学的全切片成像 (WSI) 数据集，包含来自三种组织类型的多个多染色组织样本。来源和结果：https://borda.github.io/BIRL