Virtual staining of multiphoton images based on CycleGAN and Pix2pix

Yuancan Li; Shanghai Jiang; Hong Lu; Le Chen; Li Ai; Xiaoyi Lin; Binbin Luo; Mingfu Zhao

doi:10.1117/12.3025205

8 February 2024 Virtual staining of multiphoton images based on CycleGAN and Pix2pix

Yuancan Li, Shanghai Jiang, Hong Lu, Le Chen, Li Ai, Xiaoyi Lin, Binbin Luo, Mingfu Zhao

Proceedings Volume 13066, International Conference on Optoelectronic Materials and Devices (ICOMD 2023); 1306611 (2024) https://doi.org/10.1117/12.3025205
Event: 2023 International Conference on Optoelectronic Materials and Devices (COMD 2023), 2023, Chongqing, China

Abstract

The microscopic evaluation of tissues is greatly aided by histological staining, which plays an important role in diagnostic pathology and life science research, but histological staining is often labor-intensive and expensive. Unlike conventional microscopy methods, multiphoton microimaging does not require any special labeling or staining of the sample prior to observation. Histologically stained pathology images can now be acquired by deep learning thanks to advancements in the field of image generation and image transformation. In this paper, we use deep learning network to perform virtual staining on the acquired multiphoton microscopy images, and the results show that the histological stained images generated by virtual staining can show most of the details in the original images, which is close to the original stained images, and it is feasible to perform virtual staining on multiphoton images using deep learning.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuancan Li, Shanghai Jiang, Hong Lu, Le Chen, Li Ai, Xiaoyi Lin, Binbin Luo, and Mingfu Zhao "Virtual staining of multiphoton images based on CycleGAN and Pix2pix", Proc. SPIE 13066, International Conference on Optoelectronic Materials and Devices (ICOMD 2023), 1306611 (8 February 2024); https://doi.org/10.1117/12.3025205

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