Robust Quad-Tree based Registration on Whole Slide Images

Christian Marzahl, Frauke Wilm, Dressler Franz F., Lars Tharun, Sven Perner, Christof A. Bertram, Christine Kröger, Jörn Voigt, Robert Klopfleisch, Andreas Maier, Marc Aubreville, Katharina Breininger
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 156:181-190, 2021.

Abstract

The registration of whole slide images (WSIs) provides the basis for many subsequent processing steps in digital pathology. For instance, the registration of immunohistochemistry (IHC) and hematoxylin & eosin (H&E)-stained WSIs is usually the first step in guiding IHC diagnostic procedures. Still, many registration methods operate poorly on WSIs. Reasons for this include the WSI size, fluctuating image quality or elastic tissue deformations. Multiple prior methods are further specialised towards a specific image modality, such as histology or cytology, or rely on a specific preparation protocol. To minimise these effects, we developed a robust WSI registration, which differs from previous methods by the following new aspect: We introduce a multi-scale approach based on a quad-tree (QT), with several termination criteria that makes the algorithm particularly insensitive to tissue artefacts and that further allows to estimate a piece-wise affine transformation. We validated our method on five scanner systems and 60 WSIs with different stainings. Our results outperformed any publicly available WSI registration method. The QT code, WSI landmarks and tools used to create the validation dataset are made publicly available.

Cite this Paper

BibTeX
@InProceedings{pmlr-v156-marzahl21a, title = {Robust Quad-Tree based Registration on Whole Slide Images}, author = {Marzahl, Christian and Wilm, Frauke and F., Dressler Franz and Tharun, Lars and Perner, Sven and Bertram, Christof A. and Kr{\"o}ger, Christine and Voigt, J{\"o}rn and Klopfleisch, Robert and Maier, Andreas and Aubreville, Marc and Breininger, Katharina}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {181--190}, year = {2021}, editor = {Atzori, Manfredo and Burlutskiy, Nikolay and Ciompi, Francesco and Li, Zhang and Minhas, Fayyaz and Müller, Henning and Peng, Tingying and Rajpoot, Nasir and Torben-Nielsen, Ben and van der Laak, Jeroen and Veta, Mitko and Yuan, Yinyin and Zlobec, Inti}, volume = {156}, series = {Proceedings of Machine Learning Research}, month = {27 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v156/marzahl21a/marzahl21a.pdf}, url = {https://proceedings.mlr.press/v156/marzahl21a.html}, abstract = {The registration of whole slide images (WSIs) provides the basis for many subsequent processing steps in digital pathology. For instance, the registration of immunohistochemistry (IHC) and hematoxylin & eosin (H&E)-stained WSIs is usually the first step in guiding IHC diagnostic procedures. Still, many registration methods operate poorly on WSIs. Reasons for this include the WSI size, fluctuating image quality or elastic tissue deformations. Multiple prior methods are further specialised towards a specific image modality, such as histology or cytology, or rely on a specific preparation protocol. To minimise these effects, we developed a robust WSI registration, which differs from previous methods by the following new aspect: We introduce a multi-scale approach based on a quad-tree (QT), with several termination criteria that makes the algorithm particularly insensitive to tissue artefacts and that further allows to estimate a piece-wise affine transformation. We validated our method on five scanner systems and 60 WSIs with different stainings. Our results outperformed any publicly available WSI registration method. The QT code, WSI landmarks and tools used to create the validation dataset are made publicly available. } }
Endnote
%0 Conference Paper %T Robust Quad-Tree based Registration on Whole Slide Images %A Christian Marzahl %A Frauke Wilm %A Dressler Franz F. %A Lars Tharun %A Sven Perner %A Christof A. Bertram %A Christine Kröger %A Jörn Voigt %A Robert Klopfleisch %A Andreas Maier %A Marc Aubreville %A Katharina Breininger %B Proceedings of the MICCAI Workshop on Computational Pathology %C Proceedings of Machine Learning Research %D 2021 %E Manfredo Atzori %E Nikolay Burlutskiy %E Francesco Ciompi %E Zhang Li %E Fayyaz Minhas %E Henning Müller %E Tingying Peng %E Nasir Rajpoot %E Ben Torben-Nielsen %E Jeroen van der Laak %E Mitko Veta %E Yinyin Yuan %E Inti Zlobec %F pmlr-v156-marzahl21a %I PMLR %P 181--190 %U https://proceedings.mlr.press/v156/marzahl21a.html %V 156 %X The registration of whole slide images (WSIs) provides the basis for many subsequent processing steps in digital pathology. For instance, the registration of immunohistochemistry (IHC) and hematoxylin & eosin (H&E)-stained WSIs is usually the first step in guiding IHC diagnostic procedures. Still, many registration methods operate poorly on WSIs. Reasons for this include the WSI size, fluctuating image quality or elastic tissue deformations. Multiple prior methods are further specialised towards a specific image modality, such as histology or cytology, or rely on a specific preparation protocol. To minimise these effects, we developed a robust WSI registration, which differs from previous methods by the following new aspect: We introduce a multi-scale approach based on a quad-tree (QT), with several termination criteria that makes the algorithm particularly insensitive to tissue artefacts and that further allows to estimate a piece-wise affine transformation. We validated our method on five scanner systems and 60 WSIs with different stainings. Our results outperformed any publicly available WSI registration method. The QT code, WSI landmarks and tools used to create the validation dataset are made publicly available.
APA
Marzahl, C., Wilm, F., F., D.F., Tharun, L., Perner, S., Bertram, C.A., Kröger, C., Voigt, J., Klopfleisch, R., Maier, A., Aubreville, M. & Breininger, K.. (2021). Robust Quad-Tree based Registration on Whole Slide Images. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 156:181-190 Available from https://proceedings.mlr.press/v156/marzahl21a.html.

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