Generative adversarial network based digital stain conversion for generating RGB EVG stained image from hyperspectral H&E stained image

Tanwi Biswas; Hiroyuki Suzuki; Masahiro Ishikawa; Naoki Kobayashi; Takashi Obi

doi:10.1117/1.JBO.28.5.056501

31 May 2023 Generative adversarial network based digital stain conversion for generating RGB EVG stained image from hyperspectral H&E stained image

Tanwi Biswas, Hiroyuki Suzuki, Masahiro Ishikawa, Naoki Kobayashi, Takashi Obi

Author Affiliations +

Journal of Biomedical Optics, Vol. 28, Issue 5, 056501 (May 2023). https://doi.org/10.1117/1.JBO.28.5.056501

Abstract

Significance

Quantification of elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. Though hematoxylin and eosin (H&E) staining is a routinely used and less expensive tissue staining technique, elastic and collagen fibers cannot be differentiated using it. So, in conventional pathology, special staining technique, such as Verhoeff’s van Gieson (EVG), is applied physically for this purpose. However, the procedure of EVG staining is very expensive and time-consuming.

Aim

The goal of our study is to propose a deep-learning-based computerized method for the generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure.

Approach

H&E stained hyperspectral image and EVG stained RGB whole slide image of human pancreatic tissue have been leveraged for this experiment. CycleGAN-based deep learning model has been proposed for digital stain conversion while images from source and target domains are of different modalities (hyperspectral and RGB) with different channel dimensions. A set of three basis functions have been introduced for calculating one of the losses of the proposed method, which retains the relevant features of EVG stained image within the reduced channel dimension of the H&E stained one.

Results

The experimental results showed that a set of three basis functions including linear discriminant function and transmittance spectrum of eosin and hematoxylin better retained the essential properties of the elastic fiber to be discriminated from collagen fiber within the reduced dimension of the hyperspectral H&E stained image. Also, only a smaller number of paired training data with our proposed training method contributed significantly to the generation of more realistic EVG stained image with more precise identification of elastic fiber.

Conclusions

RGB EVG stained image is generated from hyperspectral H&E stained image for which our model has performed two types of image conversion simultaneously: hyperspectral to RGB and H&E to EVG. The experimental results show that the intentionally designed set of three basis functions contains more relevant information and prove the effectiveness of our proposed method in generating realistic RGB EVG stained image from hyperspectral H&E stained one.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Tanwi Biswas, Hiroyuki Suzuki, Masahiro Ishikawa, Naoki Kobayashi, and Takashi Obi "Generative adversarial network based digital stain conversion for generating RGB EVG stained image from hyperspectral H&E stained image," Journal of Biomedical Optics 28(5), 056501 (31 May 2023). https://doi.org/10.1117/1.JBO.28.5.056501

Received: 17 March 2023; Accepted: 15 May 2023; Published: 31 May 2023

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KEYWORDS

Hyperspectral imaging

RGB color model

Elasticity

Education and training

Tissues

Image quality

Collagen

Significance

Aim

Approach

Results

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