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Generation of RGB EVG stained image from hyperspectral H&E stained image using generative adversarial network (GAN)

Tanwi Biswas,Hiroyuki Suzuki,M. Ishikawa,Naoki Kobayashi,T. Obi

Published 2023 in Medical Imaging

ABSTRACT

Quantifying elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. In conventional pathology, special staining technique such as EVG (Verhoeff’s Van Gieson) is applied physically for this purpose which is expensive and time-consuming procedure. Though H&E (Hematoxylin and Eosin) staining is routinely used, less expensive and most common tissue staining technique, elastic and collagen fibers cannot be differentiated using it. This study proposes a modified CycleGAN based unsupervised method for the computerized generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure. Our proposed method is designed to utilize the sufficient spectral information provided by the H&E hyperspectral image (HSI) without reducing the spectral dimension. For doing so, we have faced challenges to calculate one of the training losses (identity loss) of CycleGAN that requires reducing the channel dimension of H&E HSI to be the same as RGB EVG stained image. We have addressed the issue by adopting intentionally designed three basis functions that can reduce the channel dimension of HSI into three without losing the essential color of elastic fibers. The set of this function includes Linear Discriminant Function (LDF) and the transmittance spectrum of Eosin and Hematoxylin which has proved to best preserve the underlying important features of EVG stained image while reducing the dimensionality of hyperspectral H&E. The experimental result proves the feasibility of our proposed method to generate realistic EVG stained image from its corresponding H&E stained one.

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    Medical Imaging

  • Publication date

    2023-04-06

  • Fields of study

    Medicine, Computer Science, Engineering

  • Identifiers
    DOI 10.1117/12.2653491
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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