Re-staining pathology images by FCNN

Masayuki Fujitani, Yoshihiko Mochizuki, Satoshi Iizuka, Edgar Simo-Serra, Hirokazu Kobayashi, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Hidekata Hontani, Hiroshi Ishikawa

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

Abstract

In histopathology, pathologic tissue samples are stained using one of various techniques according to the desired features to be observed in microscopic examination. One problem is that staining is irreversible. Once a tissue slice is stained using a technique, it cannot be re-stained using another. In this work, we propose a method for simulated re-staining using a Fully Convolutional Neural Network (FCNN). We convert a digitally scanned pathology image of a sample, stained using one technique, into another image with a different simulated stain. The challenge is that the ground truth cannot be obtained: The network needs training data, which in this case would be pairs of images of a sample stained in two different techniques. We overcome this problem by using the images of consecutive slices that are stained using the two distinct techniques, screening for morphological similarity by comparing their density components in the HSD color space. We demonstrate the effectiveness of the method in the case of converting hematoxylin and eosin-stained images into Masson's trichrome-stained images.

Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Machine Vision Applications, MVA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9784901122184
DOIs
Publication statusPublished - 2019 May
Event16th International Conference on Machine Vision Applications, MVA 2019 - Tokyo, Japan
Duration: 2019 May 272019 May 31

Publication series

NameProceedings of the 16th International Conference on Machine Vision Applications, MVA 2019

Conference

Conference16th International Conference on Machine Vision Applications, MVA 2019
CountryJapan
CityTokyo
Period19/5/2719/5/31

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing
  • Computer Vision and Pattern Recognition

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  • Cite this

    Fujitani, M., Mochizuki, Y., Iizuka, S., Simo-Serra, E., Kobayashi, H., Iwamoto, C., Ohuchida, K., Hashizume, M., Hontani, H., & Ishikawa, H. (2019). Re-staining pathology images by FCNN. In Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019 [8757875] (Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/MVA.2019.8757875