medical-2

BIRL

3260 papers • 126 benchmarks • 313 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.

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Benchmarks

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Most implemented papers

Consistent and Elastic Registration of Histological Sections Using Vector-Spline Regularization

Ignacio Arganda-Carreras, C. Sorzano, R. Marabini, J. Carazo, C. Ortíz-de-Solórzano, J. Kybic•Thu May 11 2006

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).

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Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain

B. Avants, C. Epstein, M. Grossman, J. Gee•Thu Jan 31 2008

This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.

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Benchmarking of Image Registration Methods for Differently Stained Histological Slides

A. Muñoz-Barrutia, J. Kybic, Jiri Borovec•Sun Sep 30 2018

Image registration is a common task for many biomedical analysis applications. The present work focuses on the benchmarking of registration methods on differently stained histological slides. This is a challenging task due to the differences in the appearance model, the repetitive texture of the details and the large image size, between other issues. Our benchmarking data is composed of 616 image pairs at two different scales - average image diagonal 2.4k and 5k pixels. We compare eleven fully automatic registration methods covering the widely used similarity measures (and optimization strategies with both linear and elastic transformation). For each method, the best parameter configuration is found and subsequently applied to all the image pairs. The performance of the algorithms is evaluated from several perspectives - the registrations (in)accuracy on manually annotated landmarks, the method robustness and its processing computation time.

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Deformable medical image registration: setting the state of the art with discrete methods.

N. Komodakis, N. Paragios, Ben Glocker, Aristeidis Sotiras•Wed Jul 13 2011

A novel deformable image registration paradigm that exploits Markov random field formulation and powerful discrete optimization algorithms is introduced, leading to a modular, powerful, and flexible formulation that can account for arbitrary image-matching criteria, various local deformation models, and regularization constraints.

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BIRL: Benchmark on Image Registration methods with Landmark validation

Jiri Borovec•Mon Dec 30 2019

A generic image registration benchmark with automatic evaluation using landmark annotations, an integration of several standard image registration methods aiming at biomedical imaging into the BIRL framework, and experimental results of these SOTA methods on the CIMA dataset.

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