Deep Learning in Biomedical Optics.

Journal: Lasers in surgery and medicine
Published Date: Aug 1, 2021

Abstract

This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC.

Authors

  • Lei Tian
    Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's Street, RM 830, Boston, Massachusetts, 02215.
  • Brady Hunt
    Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, 03755.
  • Muyinatu A Lediju Bell
  • Ji Yi
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21218.
  • Jason T Smith
    Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180; smithj28@rpi.edu intesx@rpi.edu.
  • Marien Ochoa
    Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, New York, 12180.
  • Xavier Intes
    Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY.
  • Nicholas J Durr

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