SHIFT: Speedy histopathological-To-immunofluorescent translation of whole slide images using conditional generative adversarial networks

Erik A. Burlingame, Adam A. Margolin, Joe W. Gray, Young Hwan Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Multiplexed imaging such as multicolor immunofluorescence staining, multiplexed immunohistochemistry (mIHC) or cyclic immunofluorescence (cycIF) enables deep assessment of cellular complexity in situ and, in conjunction with standard histology stains like hematoxylin and eosin (H and E), can help to unravel the complex molecular relationships and spatial interdependencies that undergird disease states. However, these multiplexed imaging methods are costly and can degrade both tissue quality and antigenicity with each successive cycle of staining. In addition, computationally intensive image processing such as image registration across multiple channels is required. We have developed a novel method, speedy histopathological-To-immunofluorescent translation (SHIFT) of whole slide images (WSIs) using conditional generative adversarial networks (cGANs). This approach is rooted in the assumption that specific patterns captured in IF images by stains like DAPI, pan-cytokeratin (panCK), or α-smooth muscle actin (α-SMA) are encoded in H and E images, such that a SHIFT model can learn useful feature representations or architectural patterns in the H and E stain that help generate relevant IF stain patterns. We demonstrate that the proposed method is capable of generating realistic tumor marker IF WSIs conditioned on corresponding H and E-stained WSIs with up to 94.5% accuracy in a matter of seconds. Thus, this method has the potential to not only improve our understanding of the mapping of histological and morphological profiles into protein expression profiles, but also greatly increase the efficiency of diagnostic and prognostic decision-making.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationDigital Pathology
EditorsMetin N. Gurcan, John E. Tomaszewski
PublisherSPIE
ISBN (Electronic)9781510616516
DOIs
StatePublished - 2018
EventMedical Imaging 2018: Digital Pathology - Houston, United States
Duration: Feb 11 2018Feb 12 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10581
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2018: Digital Pathology
CountryUnited States
CityHouston
Period2/11/182/12/18

Keywords

  • Conditional generative adversarial network
  • Deep learning
  • Digital pathology
  • Image translation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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