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2019 | OriginalPaper | Chapter

Virtually Redying Histological Images with Generative Adversarial Networks to Facilitate Unsupervised Segmentation: A Proof-of-Concept Study

Authors : Michael Gadermayr, Barbara M. Klinkhammer, Peter Boor

Published in: Digital Pathology

Publisher: Springer International Publishing

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Abstract

Approaches relying on adversarial networks facilitate image-to-image-translation based on unpaired training and thereby open new possibilities for special tasks in image analysis. We propose a methodology to improve segmentability of histological images by making use of image-to-image translation. We generate virtual stains and exploit the additional information during segmentation. Specifically a very basic pixel-based segmentation approach is applied in order to focus on the information content available on pixel-level and to avoid any bias which might be introduced by more elaborated techniques. The results of this proof-of-concept trial indicate a performance gain compared to segmentation with the source stain only. Further experiments including more powerful supervised state-of-the-art machine learning approaches and larger evaluation data sets need to follow.

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previous chapter Patch Clustering for Representation of Histopathology Images

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

We use the provided PyTorch reference implementation [12].

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Title: Virtually Redying Histological Images with Generative Adversarial Networks to Facilitate Unsupervised Segmentation: A Proof-of-Concept Study
Authors: Michael Gadermayr
Barbara M. Klinkhammer
Peter Boor
Publisher: Springer International Publishing
Book: Digital Pathology
Print ISBN: 978-3-030-23936-7

Electronic ISBN: 978-3-030-23937-4

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-23937-4_5

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