Conditional generative adversarial network (cGAN) for synthesis of digital histologic images from hyperspectral images

Ling Ma; Jeremy Sherey; Doreen Palsgrove; Baowei Fei

doi:10.1117/12.2653715

6 April 2023 Conditional generative adversarial network (cGAN) for synthesis of digital histologic images from hyperspectral images

Ling Ma, Jeremy Sherey, Doreen Palsgrove, Baowei Fei

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Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124711D (2023) https://doi.org/10.1117/12.2653715
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

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Abstract

The aim of this study is to develop a method to transform hyperspectral images of H&E-stained slides to natural-color RGB histology images for easy visualization. Hyperspectral images were obtained at 40× objective magnification and downsampled by various factors to generate data equivalent to different magnifications. High-resolution digital histologic RGB images were cropped and registered to the corresponding hyperspectral images as the ground truth. A conditional generative adversarial network (cGAN) was trained to output natural color RGB images of the histological tissue samples. The generated synthetic RGBs have similar color and sharpness to real RGBs. Image classification was implemented using the real and synthetic RGBs, respectively, with a pretrained network. The classification of tumor and normal tissue using the HSI-synthesized RGBs yielded a comparable but slightly higher accuracy and AUC than the real RGBs.

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Ling Ma, Jeremy Sherey, Doreen Palsgrove, and Baowei Fei "Conditional generative adversarial network (cGAN) for synthesis of digital histologic images from hyperspectral images", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124711D (6 April 2023); https://doi.org/10.1117/12.2653715

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