Prof. Hiroyuki Suzuki Profile

Prof. Hiroyuki Suzuki

at Gunma Univ

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Publications (4)

SPIE Journal Paper | 31 May 2023 Open Access

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

JBO, Vol. 28, Issue 05, 056501, (May 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.5.056501

KEYWORDS: Hyperspectral imaging, RGB color model, Elasticity, Education and training, Tissues, Image quality, Collagen, Digital imaging, Data modeling, Data conversion

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SignificanceQuantification 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.AimThe 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.ApproachH&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.ResultsThe 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.ConclusionsRGB 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.

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Proceedings Article | 7 April 2023 Presentation + Paper

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

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

Proceedings Volume 12471, 124710D (2023) https://doi.org/10.1117/12.2653491

KEYWORDS: Hyperspectral imaging, Tissues, Histopathology, Deep learning, Image processing

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Proceedings Article | 27 October 2021 Paper

Finger region extraction using color of skin for hand-waving finger vein authentication

Hiroyuki Suzuki, Narissa Ditthapakdijanya, Takashi Komuro, Keiichiro Kagawa, Kazuya Nakano, Takashi Obi

Proceedings Volume 11925, 119250X (2021) https://doi.org/10.1117/12.2615690

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Effect of formalin fixing on chromophore saliency maps derived from multi-spectral macropathology skin images

Eleni Aloupogianni, Hiroyuki Suzuki, Takaya Ichimura, Atsushi Sasaki, Hiroto Yanagisawa, Tetsuya Tsuchida, Masahiro Ishikawa, Naoki Kobayashi, Takashi Obi

Proceedings Volume 11603, 116030W (2021) https://doi.org/10.1117/12.2581926

KEYWORDS: Skin, Chromophores, Tissues, Multispectral imaging, Hemodynamics, Absorption, Statistical analysis, RGB color model, Pathology, In vivo imaging

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