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oai:uvadoc.uva.es:10324/425842021-06-23T09:52:46Zcom_10324_1134com_10324_931com_10324_894col_10324_1213

Lotz, J. M. Olesch, J. Muller, Benedikt Polzin, T. Galuschka, P. Lotz, J. M. Heldmann, S Laue, H González-Vallinas Garrachón, Margarita Warth, Arne Lahrmann, Bernd Grabe, N Sedlaczek, Oliver Breuhahn, Kai Modersitzki, J 2020-09-28T05:46:07Z 2020-09-28T05:46:07Z 2016 IEEE Transactions on biomedical engineering, 2016, Vol. 63, n. 9, p.1812-1819 0018-9294 http://uvadoc.uva.es/handle/10324/42584 10.1109/TBME.2015.2503122 1812 9 1819 IEEE Transactions on Biomedical Engineering 63 1558-2531 Image registration of whole slide histology images allows the fusion of fine-grained information-like different immunohistochemical stains-from neighboring tissue slides. Traditionally, pathologists fuse this information by looking subsequently at one slide at a time. If the slides are digitized and accurately aligned at cell level, automatic analysis can be used to ease the pathologist's work. However, the size of those images exceeds the memory capacity of regular computers. Methods: We address the challenge to combine a global motion model that takes the physical cutting process of the tissue into account with image data that is not simultaneously globally available. Typical approaches either reduce the amount of data to be processed or partition the data into smaller chunks to be processed separately. Our novel method first registers the complete images on a low resolution with a nonlinear deformation model and later refines this result on patches by using a second nonlinear registration on each patch. Finally, the deformations computed on all patches are combined by interpolation to form one globally smooth nonlinear deformation. The NGF distance measure is used to handle multistain images. Results: The method is applied to ten whole slide image pairs of human lung cancer data. The alignment of 85 corresponding structures is measured by comparing manual segmentations from neighboring slides. Their offset improves significantly, by at least 15%, compared to the low-resolution nonlinear registration. Conclusion/Significance: The proposed method significantly improves the accuracy of multistain registration which allows us to compare different antibodies at cell level. eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ © IEEE Attribution-NonCommercial-NoDerivatives 4.0 Internacional Attribution-NonCommercial-NoDerivatives 4.0 Internacional Patch-based nonlinear image registration for gigapixel whole slide images info:eu-repo/semantics/article