references.bib

@inproceedings{Benchmark2018,
    abstract = {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.},
    address = {Athens},
    author = {Borovec, Jiri and Munoz-Barrutia, Arrate and Kybic, Jan},
    booktitle = {IEEE International Conference on Image Processing (ICIP)},
    doi = {10.1109/ICIP.2018.8451040},
    file = {:home/jirka/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Borovec, Munoz-Barrutia, Kybic - 2018 - Benchmarking of image registration methods for differently stained histological slides(2).pdf:pdf},
    isbn = {978-1-4799-7061-2},
    keywords = {Image registration,benchmarking,histological slides,pathology,stained tissue},
    mendeley-groups = {BioImaging/img registr.},
    mendeley-tags = {Image registration,benchmarking,histological slides,pathology,stained tissue},
    pages = {3368--3372},
    title = {{Benchmarking of Image Registration Methods for Differently Stained Histological Slides}},
    url = {ftp://cmp.felk.cvut.cz/pub/cmp/articles/borovec/Borovec-ICIP2018.pdf https://ieeexplore.ieee.org/document/8451040/},
    year = {2018}
}
@report{BIRL2019,
    title={BIRL: Benchmark on Image Registration methods with Landmark validation},
    author={Jiri Borovec},
    year={2019},
    eprint={1912.13452},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    ee={https://arxiv.org/abs/1912.13452},
}
@ARTICLE{ANHIR2020,
    author={J. {Borovec} and J. {Kybic} and I. {Arganda-Carreras} and D. V. {Sorokin} and G. {Bueno} and A. V. {Khvostikov} and S. {Bakas} and E. I. {Chang} and S. {Heldmann} and K. {Kartasalo} and L. {Latonen} and J. {Lotz} and M. {Noga} and S. {Pati} and K. {Punithakumar} and P. {Ruusuvuori} and A. {Skalski} and N. {Tahmasebi} and M. {Valkonen} and L. {Venet} and Y. {Wang} and N. {Weiss} and M. {Wodzinski} and Y. {Xiang} and Y. {Xu} and Y. {Yan} and P. {Yushkevic} and S. {Zhao} and A. {Muñoz-Barrutia}}, journal={IEEE Transactions on Medical Imaging},
    title={ANHIR: Automatic Non-rigid Histological Image Registration Challenge},
    year={2020},
    volume={},
    number={},
    pages={1-1},
    abstract={Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner.
     We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered.
     Registration accuracy was evaluated using manually placed landmarks.
     In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop.
     Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods.
     The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand.
     They outperformed off-the-shelf methods, mostly by being more robust.
     The best methods could successfully register over 98 % of all landmarks and their mean landmark registration accuracy (TRE) was 0.44 % of the image diagonal.
     The challenge remains open to submissions and all images are available for download.},
    keywords={Image registration;Microscopy},
    doi={10.1109/TMI.2020.2986331},
    ISSN={1558-254X},
    month={},
}