TY - JOUR
T1 - Patch-Based Nonlinear Image Registration for Gigapixel Whole Slide Images
AU - Lotz, J M
AU - Olesch, J
AU - Müller, Benedikt
AU - Polzin, Thomas
AU - Galuschka, P.
AU - Laue, Hendrik
AU - Warth, Arne
AU - Lahrmann, Bernd
AU - Grabe, Niels
AU - Sedlaczek, Oliver
AU - Breuhahn, Kai
AU - Heldmann, Stefan
AU - Modersitzki, Jan
AU - Lotz, Johannes
AU - González-Vallinas, Margarita
PY - 2015/9/1
Y1 - 2015/9/1
N2 - Objective: 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 multi-stain 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 multi-stain registration which allows to compare different anti-bodies at cell-level. 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 multi-stain 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 multi-stain registration which allows to compare different anti-bodies at cell-level.
AB - Objective: 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 multi-stain 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 multi-stain registration which allows to compare different anti-bodies at cell-level. 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 multi-stain 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 multi-stain registration which allows to compare different anti-bodies at cell-level.
UR - https://www.researchgate.net/publication/284562509_Patch-Based_Nonlinear_Image_Registration_for_Gigapixel_Whole_Slide_Images
U2 - 10.1109/TBME.2015.2503122
DO - 10.1109/TBME.2015.2503122
M3 - Journal articles
SN - 0018-9294
VL - 63
SP - 1812
EP - 1819
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 9
ER -