Single fiber OCT imager for breast tissue classification based on deep learning

Document Type

Conference Proceeding

Publication Date

1-1-2020

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.

Identifier

85082727387 (Scopus)

ISBN

[9781510632295]

Publication Title

Progress in Biomedical Optics and Imaging Proceedings of SPIE

External Full Text Location

https://doi.org/10.1117/12.2547015

ISSN

16057422

Volume

11233

Grant

1R15CA213092-01A1

Fund Ref

National Institutes of Health

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