Maxime Sermesant

Cardiac Imaging, Biophysical Modeling & Machine Learning

My research work combines biophysical and statistical modeling with clinical data in order to help diagnosis and therapy planning. This is by nature a very collaborative and multi-disciplinary work at the intersection of academic, clinical and industrial environments.


Selected Publications

Patient-Specific Models

  1. Sophie Giffard-Roisin, Thomas Jackson, Lauren Fovargue, Jack Lee, Hervé Delingette, Reza Razavi, Nicholas Ayache, and Maxime Sermesant.Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping. IEEE Transactions on Biomedical Engineering, 64(9):2206 – 2218, September 2017. 
  2. Rocìo Cabrera-Lozoya, Benjamin Berte, Hubert Cochet, Pierre Jaïs, Nicholas Ayache, and Maxime Sermesant. Image-based Biophysical Simulation of Intracardiac Abnormal Ventricular Electrograms. IEEE Transactions on Biomedical Engineering, PP(99), 2016.
  3. Zhong Chen, Rocìo Cabrera-Lozoya, Jatin Relan, Manav Sohal, Anoop Shetty, Rashed Karim, Hervé Delingette, Jaswinder Gill, Kawal Rhode,Nicholas Ayache, Peter Taggart, Christopher Aldo Rinaldi, Maxime Sermesant, and Reza Razavi. Biophysical modelling predicts ventricular tachycardia inducibility and circuit morphology: A combined clinical validation and computer modelling approach. Journal of Cardiovascular Electrophysiology, 27(7):851-860, 2016.
  4. Stéphanie Marchesseau, Hervé Delingette, Maxime Sermesant, Rocio Cabrera Lozoya, Catalina Tobon-Gomez, Philippe Moireau, Rosa Maria Figueras I Ventura, Karim Lekadir, Alfredo Hernandez, Mireille Garreau, Erwan Donal, Christophe Leclercq, Simon G. Duckett, Kawal Rhode, Christopher Aldo Rinaldi, Alejandro F. Frangi, Reza Razavi, Dominique Chapelle, and Nicholas Ayache. Personalization of a Cardiac Electromechanical Model using Reduced Order Unscented Kalman Filtering from Regional Volumes. Medical Image Analysis, 17(7):816-829, May 2013.
  5. Maxime Sermesant, R. Chabiniok, P. Chinchapatnam, Tommaso Mansi, F. Billet, P. Moireau, Jean-Marc Peyrat, Ken C. L. Wong, Jatin Relan, Kawal Rhode, M. Ginks, P. Lambiase, Hervé Delingette, M. Sorine, C. Aldo Rinaldi, D. Chapelle, Reza Razavi, and Nicholas Ayache. Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: A preliminary clinical validation. Medical Image Analysis, 16(1):201-215, 2012.

Personalisation Methodology

  1. Roch Molléro, Xavier Pennec, Hervé Delingette, Alan Garny, Nicholas Ayache, and Maxime Sermesant. Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models. Biomechanics and Modeling in Mechanobiology, pp 1-16, September 2017.
  2. Radomir Chabiniok, Vicky Y. Wang, Myrianthi Hadjicharalambous, Liya Asner, Jack Lee, Maxime Sermesant, Ellen Kuhl, Alistair A. Young, Philippe Moireau, Martyn P. Nash, Dominique Chapelle, and David Nordsletten. Multiphysics and multiscale modelling, data-model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics. Interface Focus, 6(2), 2016.
  3. Stéphanie Marchesseau, Hervé Delingette, Maxime Sermesant, Michel Sorine, Kawal Rhode, Simon G. Duckett, Christopher Aldo Rinaldi, Reza Razavi, and Nicholas Ayache. Preliminary Specificity Study of the Bestel-Clément-Sorine Electromechanical Model of the Heart using Parameter Calibration from Medical Images. Journal of mechanical behavior of biomedical materials, 20:259-271, 2013.
  4. E. Konukoglu, J. Relan, U. Cilingir, B. Menze, P. Chinchapatnam, A. Jadidi, H. Cochet, M. Hocini, H. Delingette, P. Jaïs, M. Haïssaguerre, N. Ayache, and M. Sermesant. Efficient Probabilistic Model Personalization Integrating Uncertainty on Data and Parameters: Application to Eikonal-Diffusion Models in Cardiac Electrophysiology. Progress in Biophysics and Molecular Biology, 107(1):134-146, October 2011.
  5. Jatin Relan, Mihaela Pop, Hervé Delingette, Graham Wright, Nicholas Ayache, and Maxime Sermesant. Personalisation of a Cardiac Electrophysiology Model using Optical Mapping and MRI for Prediction of Changes with Pacing. IEEE Transactions on Bio-Medical Engineering, 58(12):3339-3349, 2011.

Group-wise Statistics

  1. Kristin Mcleod, Maxime Sermesant, Philipp Beerbaum, and Xavier Pennec. Spatio-Temporal Tensor Decomposition of a Polyaffine Motion Model for a Better Analysis of Pathological Left Ventricular Dynamics. IEEE Transactions on Medical Imaging, 34(7):1562-1675, July 2015.
  2. Romain Guibert, Kristin Mcleod, Alfonso Caiazzo, Tommaso Mansi, Miguel Angel Fernández, Maxime Sermesant, Xavier Pennec, Irene Vignon-Clementel, Younes Boudjemline, and Jean-Frédéric Gerbeau. Group-wise Construction of Reduced Models for Understanding and Characterization of Pulmonary Blood Flows from Medical Images. Medical Image Analysis, 18(1):63-82, 2014.
  3. Nicolas Toussaint, Christian T. Stoeck, Tobias Schaeffter, Sebastian Kozerke, Maxime Sermesant, and Philip G. Batchelor. In vivo human cardiac fibre architecture estimation using shape-based diffusion tensor processing. Medical Image Analysis, 2013.
  4. Jan L. Bruse, Abbas Khushnood, Kristin Mcleod, Giovanni Biglino, Maxime Sermesant, Xavier Pennec, Andrew M. Taylor, Tain-Yen Hsia, and Silvia Schievano. How successful is successful? Aortic arch shape after successful aortic coarctation repair correlates with left ventricular function.Journal of Thoracic and Cardiovascular Surgery, 153(2):418 – 427, February 2017.

Videos from Recent Invited Talks

At the Fields Institute, Toronto, CA

Recent Projects

  • MD Paedigree: Model-Driven Paediatric European Digital Repository.
  • VP2HF: Virtual Physiological Human for Heart Failure.
  • ERC MedYMA: Biophysical Modeling & Analysis of Dynamic Medical Images.
  • Euheart: an integrated project on cardiac modeling and its application for improved diagnosis and therapy planning.
  • Virtual Physiological Human: a network of excellence for collaborative investigation of the human body as a single complex system.
  • Health e-Child: an integrated healthcare platform for European paediatrics.

Research Topics: Computer Models, Group-wise Statistics and Medical Image Processing

I first developed an electromechanical model of the heart which was usable as prior knowledge in image processing tasks. It was a generic model which could provide some physiological constraints but its parameters were not adjusted to the patient. Since then, my research focus followed two main axes. First I developed methods to personalise such an electromechanical model of the heart to the clinical data of a patient, in order to help diagnosis and therapy planning. Second, I integrated biophysical and statistical methods in order to be able to model the cardiac function at a group level.

All along these years, my research has gone back and forth between modelling and imaging, and the most recent work was in combining computational physiology and computational anatomy. This is a very exciting area that integrates two different ways of targeting patient-specific medicine. On one hand, computational physiology tries to build a computer model of the patient based on biophysical models of the human body. On the other hand, computational anatomy aims at statistical learning from healthy and pathological groupwise data, in order to evaluate a specific patient against the model.

These two research areas have undergone a tremendous progress over the last decade, which can be clearly seen by the number of related publications and conferences. Milestones were achieved in developing new methods for adjusting generic models to patient data and for computing statistics on complex objects like shapes and deformations. The parallel development of computational power and numerical strategies now opens new avenues for the application and integration of these results.

I really see the biophysical and statistical approaches as complementary, because independently they lack important features. For the biophysical approach, without a statistical analysis of the studied population it is often impossible to know which are the important phenomena to model, among the multi-scale multi-physics features of the cardiac function. On the other hand, once computed, a statistical model is very hard to interpret without some mechanistic insights on the phenomena observed, and biophysical models are a great tool to explore such mechanisms.

You can have more details on my background in my Vitae.

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