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Detecting mouse squamous cell carcinoma from submicron full‐field optical coherence tomography images by deep learning
Author(s) -
Ho ChiJui,
CalderonDelgado Manuel,
Chan ChinCheng,
Lin MingYi,
Tjiu JengWei,
Huang ShengLung,
Chen Homer H.
Publication year - 2021
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.202000271
Subject(s) - optical coherence tomography , basal cell , computer science , artificial intelligence , biomedical engineering , deep learning , histopathology , tomography , optics , pathology , medicine , physics
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.