The French Society for Biomedical Engineering (SFGBM) has just given its 2016 Thesis Research Award to Matthieu Lê for his thesis written under the direction of Nicholas Ayache and Hervé Delingette of the Asclepios team at Inria’s Sophia Antipolis-Méditerranée research centre. The Asclepios team is part of the Inria@SilivonValley associate team Geomstats with Stanford University.
The “young researcher” competition, part of the “Research in Health Technologies and Imaging Days”, is open to all registered masters and PhD students in the field of biomedical engineering.
The Thesis Research Awards pays tribute to the best research work in the field in France to acknowledge young postdocs who actively seek to engage in research in the public or private sectors..
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Matthieu, can you tell us something about your educational background?
I have an engineer degree from the École Centrale Paris with a major in applied mathematics. After completing a master’s degree at ENS Cachan where I took classes with Hervé Delingette and Xavier Pennec (who are both directors of research at Inria Sophia Antipolis – Méditerranée), I decided to focus on the analysis of medical images, and the design of biophysical models. My first experience at Inria was an internship in graduate school working with anatomical and physiological data from MRIs to model the growth of brain tumours. Afterwards, I wrote a thesis on personalizing the brain tumor growth model for radiation therapy use.
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Please tell us about the subject of the presentation for which you received the Young Scientist Award at the prestigious MICCAI conference?
I submitted a paper in Munich at the 2015 MICCAI (Medical Image Computing and Computer Assisted Intervention) conference, which is the best known in the field of medical image computing. I presented a method for estimating uncertainty when planning radiation therapy due to the segmentation of the tumor by the clinician.
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How did you become interested in this field of research?
My interest in mathematics goes back to high school. In engineering school, I was particularly interested in applications: how to use sophisticated methods to solve concrete and complex problems. With my interest in computer vision and a desire to tackle problems with a potentially significant social impact, specializing in medical imaging came naturally.
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Can you provide a brief overview of the research for which you received the GBM award?
During my thesis, I worked on modeling the growth of a glioblastoma, a particularly aggressive brain tumor. To be more specific, I came up with a method to personalize the growth model for a given patient using available MRI data. The method takes into account various uncertainties due to segmentation of the tumor by the clinician and the model parameters. Lastly, I combined this method with a model of how tissues respond to radiation therapy to make radiation therapy planning more automatic and personalized.
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Where do you see your career heading from here?
I’m currently working as an engineer at Arterys, a young company in the Silicon Valley near Stanford whose CEO I met at the MICCAI conference. The company is developing artificial intelligence for cardiac imaging. I try to use advance mathematical tools to facilitate the work of the radiologist. I chose to continue in the business world because it offers more direct contact and a greater impact on clinical practice.
