Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology

Martin Halicek; Samuel Ortega; Himar Fabelo; Carlos Lopez; Marylene Lejeune; Gustavo M. Callico; Baowei Fei

doi:10.1117/12.2549994

16 March 2020 Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology

Martin Halicek, Samuel Ortega, Himar Fabelo, Carlos Lopez, Marylene Lejeune, Gustavo M. Callico, Baowei Fei

Author Affiliations +

Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology; 113200U (2020) https://doi.org/10.1117/12.2549994
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Hyperspectral imaging (HSI), which acquires up to hundreds of bands, has been proposed as a promising imaging modality for digitized histology beyond RGB imaging to provide more quantitative information to assist pathologists with disease detection in samples. While digitized RGB histology is quite standardized and easy to acquire, histological HSI often requires custom-made equipment and longer imaging times compared to RGB. In this work, we present a dataset of corresponding RGB digitized histology and histological HSI of breast cancer, and we develop a conditional generative adversarial network (GAN) to artificially synthesize HSI from standard RGB images of normal and cancer cells. The results of the GAN synthesized HSI are promising, showing structural similarity (SSIM) of approximately 80% and mean absolute error (MAE) of 6 to 11%. Further work is needed to establish the ability of generating HSI from RGB images on larger datasets.

Citation Download Citation

Martin Halicek, Samuel Ortega, Himar Fabelo, Carlos Lopez, Marylene Lejeune, Gustavo M. Callico, and Baowei Fei "Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200U (16 March 2020); https://doi.org/10.1117/12.2549994

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