Automatic recognition of tumor region in multiphoton images of hepatocellular carcinoma using a convolutional neural network

Zheng Zhang; Xunbin Yu; Xiong Zhang; Jianxin Chen; Yannan Bai; Lianhuang Li

doi:10.1117/12.2687440

27 November 2023 Automatic recognition of tumor region in multiphoton images of hepatocellular carcinoma using a convolutional neural network

Zheng Zhang, Xunbin Yu, Xiong Zhang, Jianxin Chen, Yannan Bai, Lianhuang Li

Author Affiliations +

Proceedings Volume 12770, Optics in Health Care and Biomedical Optics XIII; 127702P (2023) https://doi.org/10.1117/12.2687440
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

The fundamental principle of hepatectomy is to entirely excise the tumor while preserving adequate functional liver tissue volume. Thus, identifying tumor and non-tumor areas swiftly can enhance the precision and efficiency of liver resection, ultimately improving patient survival rates. In this study, we utilized multiphoton microscopy (MPM) to label-free identify liver tumor and non-tumor regions, following by automated classification with an open-source convolutional neural network, ResNet. The outcomes demonstrate that the network model can automatically and effectively distinguish tumor and non-tumor regions without human recognition, and MPM combining with deep learning may serve as an auxiliary tool for rapidly detection of hepatocellular carcinoma and aiding in liver resection treatment.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zheng Zhang, Xunbin Yu, Xiong Zhang, Jianxin Chen, Yannan Bai, and Lianhuang Li "Automatic recognition of tumor region in multiphoton images of hepatocellular carcinoma using a convolutional neural network", Proc. SPIE 12770, Optics in Health Care and Biomedical Optics XIII, 127702P (27 November 2023); https://doi.org/10.1117/12.2687440

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