I am currently leading the Image-Guided Therapy and Robotics research group at the Siemens Healthineers Digital Technology and Innovation center, led by Dorin Comaniciu, (Princeton, NJ, USA). My research focus covers artificial intelligence, computational physiology and robotics, with the goal to develop solutions to enable next generation image-guided procedures.I graduated in 2010 from INRIA Sophia Antipolis, France.
With the team, we won the R&D 100 award for Siemens eSie Valves, recognized among the top 100 innovative products of 2015. We had the honor to win the 2015 NJ Thomas Alva Edison Patent Award for our work on personalized simulation of mitral valve interventions, and the 2019 NJ Thomas Edison Patient Award, for our work on patient-specific simulation of cardiac electrophysiology. Our research also led to three MICCAI Young Scientists Awards in 2011 (Towards Patient-Specific Finite-Element Simulation of Mitral Clip Procedure), 2013 (Fast data-driven calibration of a cardiacelectrophysiology model from images and ECG) and 2018 (Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation). I also had the honor to present our work at the prestigious College de France in Paris in 2019.
It is with a great honor that I received the very prestigious Siemens Inventor of the Year 2020 award along with other Siemens fellows. I am so humbled to be part of Siemens Inventors family, who made so many contributions to the society. This would not have been possible without all the colleagues, mentors and collaborators with whom I had the chance to work with, with whom we strived to realize our vision of a digital twin of the heart, such a potentially disruptive technology. I am so grateful to all of them, this award is for them! Thank you everyone, let us keep pushing the limits of the technology and make digital twins a healthcare reality!
Heartfelt congratulations to Felix Meister and team for winning the best oral presentation award at the STACOM 2020 workshop, a satellite event of MICCAI 2020. The award was given for his work on “Graph convolutional regression of cardiac depolarization from sparse endocardial maps”.
In this paper, we show how geometric deep learning can be used to extrapolate electrophysiology signals throughout the myocardium given sparse and noisy endocardial measurements. A big thank you to our collaborators from JHU (Baltimore, MD), Dr. Ashikaga and Dr. Halperin, as well as the Pattern Recognition Lab at FAU (Erlangen, Germany). Congratulations to the entire team!
Our new book presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient’s heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications.
The content of the book is the result of many years of research and hard work from the team, pushing the limit of personalized computational modeling of the heart. So proud of the team! Congratulations to everyone for this achievement! I hope you will enjoy reading the as much as we enjoyed writing it!