Detecting mouse squamous cell carcinoma from submicron full-field optical coherence tomography images by deep learning.

Journal: Journal of biophotonics
Published Date: Jan 1, 2021

Abstract

The standard medical practice for cancer diagnosis requires histopathology, which is an invasive and time-consuming procedure. Optical coherence tomography (OCT) is an alternative that is relatively fast, noninvasive, and able to capture three-dimensional structures of epithelial tissue. Unlike most previous OCT systems, which cannot capture crucial cellular-level information for squamous cell carcinoma (SCC) diagnosis, the full-field OCT (FF-OCT) technology used in this paper is able to produce images at sub-micron resolution and thereby facilitates the development of a deep learning algorithm for SCC detection. Experimental results show that the SCC detection algorithm can achieve a classification accuracy of 80% for mouse skin. Using the sub-micron FF-OCT imaging system, the proposed SCC detection algorithm has the potential for in-vivo applications.

Authors

  • Chi-Jui Ho
    Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
  • Manuel Calderon-Delgado
    Graduate Institute of Photonics and Optoelectronics, National Taiwan University, Taipei, Taiwan.
  • Chin-Cheng Chan
    Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
  • Ming-Yi Lin
    Department of Dermatology, National Taiwan University Hospital, Taipei, Taiwan.
  • Jeng-Wei Tjiu
    Department of Dermatology, National Taiwan University Hospital, Taipei, Taiwan.
  • Sheng-Lung Huang
    Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
  • Homer H Chen
    Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.

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