M jermyn

Michael Jermyn

Adjunct Assistant Professor of Engineering

Overview

Michael Jermyn, PhD, is an expert in computational solutions for biomedical applications. He has experience in machine learning, cancer imaging, software development, computer vision, optical imaging, and biomedical engineering.

Education

  • BS, Computer Science and Mathematics, Tufts University 2007
  • MS, Mathematics, Tufts University 2009
  • PhD, Biomedical Engineering, Dartmouth 2013

Research Interests

Cancer imaging; machine learning; software development; optical imaging; 3D visualization/analysis

Selected Publications

  • J. Desroches, M. Jermyn, M. Pinto, F. Picot, M.A. Tremblay, S. Obaid, E. Marple, K. Urmey, D. Trudel, G. Soulez, M.C. Guiot, B. Wilson, K. Petrecca, F. Leblond, A new method using Raman spectroscopy for in vivo targeted brain cancer tissue biopsy. Scientific Reports 8(1), 1792 (2018), doi:10.1038/s41598-018-20233-3.
  • M. Jermyn, J. Mercier, K. Aubertin, J. Desroches, K. Urmey, J. Karamchandiani, E. Marple, M.C. Guiot, F. Leblond, K. Petrecca, Highly accurate detection of cancer in situ with intraoperative, label-free, multimodal optical spectroscopy. Cancer Research 77(14), 1-9 (2017), doi:10.1158/0008-5472.CAN-17-0668.
  • M. Jermyn, J. Desroches, J. Mercier, K. Aubertin, K. St-Arnaud, J. Madore, E. De Montigny, M.C. Guiot, D. Trudel, B. Wilson, K. Petrecca, F. Leblond, A review of Raman spectroscopy advances with an emphasis on clinical translation challenges in oncology. Physics in Medicine and Biology 61 R370-R400 (2016), doi:10.1088/0031-9155/61/23/R370. [Invited review paper]
  • M. Jermyn, K. Mok, J. Mercier, J. Desroches, J. Pichette, K. Saint-Arnaud, L. Bernstein, M.C. Guiot, K. Petrecca, F. Leblond, Intraoperative brain cancer detection with Raman spectroscopy in humans. Science Translational Medicine 7, 274ra19 (2015), doi:10.1126/scitranslmed.aaa2384.
  • M. Jermyn, J. Desroches, J. Mercier, M.A. Tremblay, K. St-Arnaud, M.C. Guiot, K. Petrecca, F. Leblond, Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifacts. Journal of Biomedical Optics 21(9), 094002 (2016), doi:10.1117/1.JBO.21.9.094002.
  • M. Jermyn, J. Desroches, J. Mercier, K. St-Arnaud, M.C. Guiot, F. Leblond, K. Petrecca, Raman spectroscopy detects distant invasive brain cancer cells centimeters beyond MRI capability in humans. Biomedical Optics Express 7(12), 5129-5137 (2016), doi:10.1364/BOE.7.005129.

Awards

  • Quebec Science #2 Discovery of the Year (2018)
  • La Recherche Technology Prize (2017)
  • Lewis Reford Fellows Award (2016)
  • Tubber Award at William Feindel Conference: 1st place (2015)

Patents

  • Method and apparatus for quantitative and depth resolved hyperspectral fluorescence and reflectance imaging for surgical guidance (Application 20160278678)
  • System and method for assessing a cancer status of biological tissue (Application 20170020460)

Entrepreneurship

  • DoseOptics LLC - Visualization of radiation therapy using Cherenkov imaging

Courses

  • ENGG 113: Image Visualization and Analysis

Research Projects

  • Cerenkov imaging in radiation therapy

    Cerenkov imaging in radiation therapy

    Radiation therapy is used to treat cancer tumors by killing the tissue with high ionizing radiation doses. Until recently it has not been possible to image the radiation dose delivered to tissue, but through Cherenkov light imaging, this delivered dose can be mapped with high resolution cameras. The research group focuses on quantification of the imaging, and developing tools which allow radiation therapy to be delivered in a safer and more validated manner.

  • Machine learning in cancer imaging

    Machine learning in cancer imaging

    Cancer tissue is often impossible to distinguish from healthy tissue during surgery. We have developed optical spectroscopy imaging and detection techniques that make use of sophisticated machine learning techniques to distinguish cancer tissue in real-time.

  • Scintillation dosimetry for quality assurance in radiotherapy

    Scintillation dosimetry for quality assurance in radiotherapy

    Radiation therapy is used to treat cancer tumors by killing the tissue with high ionizing radiation doses. Modern external beam radiotherapy systems deliver high dose levels to precisely marked tumor volume in less time. As a mis-administration can have potentially severe impact to the surrounding healthy tissue, more stringent and complex quality assurance measurements are required in clinics. By developing a comprehensive optical dose imaging camera system, we aim to fundamentally simplify the quality assurance process and, in turn, to further promote the culture of safety in radiotherapy. By converting the dose to visible light using scintillation phantom, we can image and reconstruct 3D dose maps in real time, enabling complete and accurate verification in a fast enough timeframe for it to be useful in every procedure.