Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach

Amal Lahiani; Jacob Gildenblat; Irina Klaman; Shadi Albarqouni; Nassir Navab; Eldad Klaiman

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

Conference

Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach

Amal Lahiani, Jacob Gildenblat, Irina Klaman, Shadi Albarqouni, Nassir Navab, Eldad Klaiman

European Congress on Digital Pathology, 2019

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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.

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Research themes

Medical Imaging Responsible AI

Keywords

Stain NormalizationHistologyPowerful RepresentationDomain AdaptationStyle Transfer

Lab authors

Amal Lahiani

Shadi Albarqouni

Nassir Navab

Cite (BibTeX)

@inproceedings{lahiani2019virtualization,
 author = {Lahiani, Amal and Gildenblat, Jacob and Klaman, Irina and Albarqouni, Shadi and Navab, Nassir and Klaiman, Eldad},
 booktitle = {European Congress on Digital Pathology},
 organization = {Springer, Cham},
 pages = {47--55},
 title = {Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach},
 year = {2019}
}