BIRL
6 papers with code • 1 benchmarks • 0 datasets
BIRL: Benchmark on Image Registration methods with Landmark validation, in particular, Biomedical image registration on WSI microscopy images of a multi-strain histology tissue sample.
Latest papers
Kernel Density Bayesian Inverse Reinforcement Learning
Inverse reinforcement learning~(IRL) is a powerful framework to infer an agent's reward function by observing its behavior, but IRL algorithms that learn point estimates of the reward function can be misleading because there may be several functions that describe an agent's behavior equally well.
BIRL: Benchmark on Image Registration methods with Landmark validation
This report presents a generic image registration benchmark with automatic evaluation using landmark annotations.
Benchmarking of image registration methods for differently stained histological slides
Image registration is a common task for many biomedical analysis applications.
Deformable medical image registration: setting the state of the art with discrete methods
To cope with both iconic and geometric (landmark-based) registration, we introduce two graphical models, one for each subproblem.
Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain
This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling.
Consistent and elastic registration of histological sections using vector-spline regularization
Here we present a new image registration algorithm for the alignment of histological sections that combines the ideas of B-spline based elastic registration and consistent image registration, to allow simultaneous registration of images in two directions (direct and inverse).
