Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach

Lahiani A, Gildenblat J, Klaman I, Albarqouni S, Navab N, Klaiman E (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 11435 LNCS

Pages Range: 47-55

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Warwick, GBR

ISBN: 9783030239367

DOI: 10.1007/978-3-030-23937-4_6

Abstract

Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology. Historically, Hematoxylin and Eosin (H&E) has been used by pathologists as a gold standard staining. However, in many cases, various target specific stains, including immunohistochemistry (IHC), are needed in order to highlight specific structures in the tissue. As tissue is scarce and staining procedures are tedious, it would be beneficial to generate images of stained tissue virtually. Virtual staining could also generate in-silico multiplexing of different stains on the same tissue segment. In this paper, we present a sample application that generates FAP-CK virtual IHC images from Ki67-CD8 real IHC images using an unsupervised deep learning approach based on CycleGAN. We also propose a method to deal with tiling artifacts caused by normalization layers and we validate our approach by comparing the results of tissue analysis algorithms for virtual and real images.

Involved external institutions

Roche Diagnostics GmbH DE Germany (DE) DeePathology Ltd. IL Israel (IL) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Lahiani, A., Gildenblat, J., Klaman, I., Albarqouni, S., Navab, N., & Klaiman, E. (2019). Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach. In Constantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 47-55). Warwick, GBR: Springer Verlag.

MLA:

Lahiani, Amal, et al. "Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach." Proceedings of the 15th European Congress on Digital Pathology, ECDP 2019, Warwick, GBR Ed. Constantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana, Springer Verlag, 2019. 47-55.

BibTeX: Download