Share Email Print
cover

Proceedings Paper

Single fiber OCT imager for breast tissue classification based on deep learning
Author(s): Yuwei Liu; Basil Hubbi; Xuan Liu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

We investigated a deep learning strategy to analyze optical coherence tomography image for accurate tissue characterization based on a single fiber OCT probe. We obtained OCT data from human breast tissue specimens. Using OCT data obtained from adipose breast tissue (normal tissue) and diseased tissue as confirmed in histology, we trained and validated a convolutional neural network (CNN) for accurate breast tissue classification. We demonstrated tumor margin identification based CNN classification of tissue at different spatial locations. We further demonstrated CNN tissue classification in OCT imaging based on a manually scanned single fiber probe. Our results demonstrated that OCT imaging capability integrated into a low-cost, disposable single fiber probe, along with sophisticated deep learning algorithms for tissue classification, allows minimally invasive tissue characterization, and can be used for cancer diagnosis or surgical margin assessment.

Paper Details

Date Published: 20 February 2020
PDF: 6 pages
Proc. SPIE 11233, Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XX, 1123313 (20 February 2020); doi: 10.1117/12.2547015
Show Author Affiliations
Yuwei Liu, New Jersey Institute of Technology (United States)
Basil Hubbi, Rutgers New Jersey Medical School (United States)
Xuan Liu, New Jersey Institute of Technology (United States)


Published in SPIE Proceedings Vol. 11233:
Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XX
Israel Gannot, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray