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

Yuwei Liu, Basil Hubbi, Xuan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationOptical Fibers and Sensors for Medical Diagnostics and Treatment Applications XX
EditorsIsrael Gannot, Israel Gannot
PublisherSPIE
ISBN (Electronic)9781510632295
DOIs
StatePublished - 2020
EventOptical Fibers and Sensors for Medical Diagnostics and Treatment Applications XX 2020 - San Francisco, United States
Duration: Feb 1 2020Feb 2 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11233
ISSN (Print)1605-7422

Conference

ConferenceOptical Fibers and Sensors for Medical Diagnostics and Treatment Applications XX 2020
CountryUnited States
CitySan Francisco
Period2/1/202/2/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Keywords

  • Artificial intelligence
  • Convolutional neural network
  • Optical coherence tomography
  • Tissue characterization

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