Deep learning in biomedical optics Lei Tian,1* Brady Hunt,2 Muyinatu A. Lediju Bell,3,4,5 Ji Yi,4,6 Jason T. Smith,7 Marien Ochoa,7 Xavier Intes,7 Nicholas J. Durr3,4* 1Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA 2Thayer School of Engineering, Dartmouth College, Hanover, NH, USA 3Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA 4Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA 5Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA 6Department of Ophthalmology, Johns Hopkins University, Baltimore, MD, USA 7Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute *Corresponding author: L. Tian,
[email protected]; N.J. Durr,
[email protected] 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 imag- ing, in vivo microscopy, widefield endoscopy, optical coher- ence tomography, photoacoustic imaging, diffuse tomogra- phy, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been ap- plied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of Fig 1: Number of reviewed research papers which utilize DL deep learning in biomedical optics are also