University of Oulu

N. Bayramoglu, M. Kaakinen, L. Eklund and J. Heikkilä, "Towards Virtual H&E Staining of Hyperspectral Lung Histology Images Using Conditional Generative Adversarial Networks," 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, 2017, pp. 64-71. doi: 10.1109/ICCVW.2017.15

Towards virtual H&E staining of hyperspectral lung histology images using conditional generative adversarial networks

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Author: Bayramoglu, Neslihan1; Kaakinen, Mika2; Eklund, Lauri2;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
2Faculty of Biochemistry and Molecular Medicine, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202002256380
Language: English
Published: Institute of Electrical and Electronics Engineers, 2017
Publish Date: 2020-02-25
Description:

Abstract

The microscopic image of a specimen in the absence of staining appears colorless and textureless. Therefore, microscopic inspection of tissue requires chemical staining to create contrast. Hematoxylin and eosin (H&E) is the most widely used chemical staining technique in histopathology. However, such staining creates obstacles for automated image analysis systems. Due to different chemical formulations, different scanners, section thickness, and lab protocols, similar tissues can greatly differ in appearance. This huge variability is one of the main challenges in designing robust and resilient automated image analysis systems. Moreover, staining process is time consuming and its chemical effects deform structures of specimens. In this work, we develop a method to virtually stain unstained specimens. Our method utilizes dimension reduction and conditional adversarial generative networks (cGANs) which build highly non-linear mappings between input and output images. Conditional GANs ability to handle very complex functions and high dimensional data enables transforming unstained hyperspectral tissue image to their H&E equivalent which comprises highly diversified appearance. In the long term, such virtual digital H&E staining could automate some of the tasks in the diagnostic pathology workflow which could be used to speed up the sample processing time, reduce costs, prevent adverse effects of chemical stains on tissue specimens, reduce observer variability, and increase objectivity in disease diagnosis.

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ISBN: 978-1-5386-1034-3
ISBN Print: 978-1-5386-1035-0
Pages: 64 - 71
DOI: 10.1109/ICCVW.2017.15
OADOI: https://oadoi.org/10.1109/ICCVW.2017.15
Host publication: 2017 IEEE International Conference on Computer Vision Workshop (ICCVW)
Conference: IEEE International Conference on Computer Vision Workshops
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
3111 Biomedicine
Subjects:
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