BIRL: Benchmark on Image Registration methods with Landmark validation
This provides a tool for researchers in biomedical imaging to compare and evaluate image registration methods, but it is incremental as it builds on existing benchmarks and methods.
The paper introduces BIRL, a benchmark for evaluating image registration methods using landmark annotations, and demonstrates its application by integrating several standard methods and testing them on the CIMA dataset for biomedical imaging.
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