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Jennifer K. Barton

Jennifer K. Barton

· Professor of Optical Sciences, Director of the BIO5 Institute

University of Arizona · Wyant College of Optical Sciences

Active 1949–2026

h-index41
Citations7.0k
Papers35662 last 5y
Funding$18.0M1 active

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Jennifer K. Barton is a Professor of Optical Sciences and the Director of the BIO5 Institute at The University of Arizona. She is also a faculty member in the Department of Electrical and Computer Engineering. Her research interests include optical imaging, specifically optical coherence tomography, laser-tissue interaction, and bioinstrumentation. Dr. Barton holds degrees from the University of Texas at Austin and the University of California at Irvine, including a Ph.D. from the University of Texas at Austin earned in 1998. She is affiliated with multiple departments and institutes, including Biomedical Engineering, Agricultural and Biosystems Engineering, and the BIO5 Institute, reflecting her interdisciplinary approach to research in optical sciences and biomedical applications.

Research topics

  • Optics
  • Biomedical engineering
  • Artificial Intelligence
  • Computer Science
  • Medicine
  • Biology
  • Pathology
  • Radiology
  • Machine Learning
  • Physics

Selected publications

  • Ovarian cancer detection using optical coherence tomography and convolutional neural networks

    Neural Computing and Applications · 2022 · 53 citations

  • Sub-millimeter endoscope demonstrates feasibility of in vivo reflectance imaging, fluorescence imaging, and cell collection in the fallopian tubes

    Journal of Biomedical Optics · 2021 · 19 citations

    Senior authorCorresponding

    SIGNIFICANCE: Most cases of high-grade serous ovarian carcinoma originate as serous tubal intraepithelial carcinoma (STIC) lesions in the fallopian tube epithelium (FTE), enabling early endoscopic detection. AIM: The cell-acquiring fallopian endoscope (CAFE) was built to meet requirements for locating potentially pathological tissue indicated by an alteration in autofluorescence or presence of a targeted fluorophore. A channel was included for directed scrape biopsy of cells from regions of inte…

  • Triple-modality co-registered endoscope featuring wide-field reflectance imaging, and high-resolution multiphoton and optical coherence microscopy

    Journal of Optical Microsystems · 2021 · 11 citations

    Senior authorCorresponding

    We present the design and feasibility testing of a multimodal co-registered endoscope based on a dual-path optical system integrated with a scanning piezo. This endoscope incorporates three different imaging modalities. A large field of view reflectance imaging system enables visualization of objects several millimeters in front of the endoscope, while optical coherence microscopy and multiphoton microscopy are employed in contact with tissue to further analyze suspicious areas. The optical syst…

  • Reengineering a falloposcope imaging system for clinical use

    Translational Biophotonics · 2020 · 10 citations

    Senior authorCorresponding

    Abstract High‐grade serous carcinoma of the ovary is believed to originate in the fallopian tubes (FTs). A submillimeter diameter endoscope with advanced imaging capabilities may take advantage of the natural pathway of the female reproductive tract to image the FTs in a minimally invasive procedure for early detection of cancer. Our lab previously built a prototype benchtop FT endoscope with pseudowhite light imaging, multispectral fluorescence imaging and optical coherence tomography. This end…

  • mmWave Radar for Sit-to-Stand Analysis: A Comparative Study With Wearables and Kinect

    IEEE Transactions on Biomedical Engineering · 2025-03-05 · 8 citations

    articleOpen access

    This study investigates a novel approach for analyzing Sit-to-Stand (STS) movements using millimeter-wave (mmWave) radar technology, aiming to develop a non-contact, privacy-preserving, and all-day operational solution for healthcare applications. A 60 GHz mmWave radar system was employed to collect radar point cloud data from 45 participants performing STS motions. Using a deep learning-based pose estimation model and Inverse Kinematics (IK), we calculated joint angles, segmented STS motions, a…

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