Maxime Sermesant

Short Bio

Maxime Sermesant is a permanent researcher at Inria, the French research institute on informatics and mathematics. His research interests include biomedical image processing, organ modelling and machine learning. The integration of these areas open up possibilities in clinical data analysis for diagnosis, and in pathology simulation for therapy planning. His main focus has been the application of patient-specific models of the heart to cardiac pathologies. He received his Diploma in General Engineering from Ecole Centrale Paris, France in 1999, his MSc from Ecole Normale Superieure de Cachan, France in 1999, and his PhD in Control, Signal and Image Processing from the University of Nice – Sophia Antipolis, France in 2003. From June 2003 to December 2005, he was a Research Fellow with the Cardiac MR Research Group, Guy’s Hospital, King’s College London, UK and since 2005, he is a Research Scientist at Inria and a visiting Lecturer at King’s College London, Division of Imaging Sciences, St Thomas’ Hospital.

Roles

Research

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.

Publications

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.

Selected Publications

Patient-Specific Models

S. Giffard-Roisin, T. Jackson, L. Fovargue, J. Lee, H. Delingette, R. Razavi, N. Ayache, M. 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.   
R. Cabrera-Lozoya, B. Berte, H. Cochet, P. Jaïs, N. Ayache, M. Sermesant. Image-based Biophysical Simulation of Intracardiac Abnormal Ventricular Electrograms. IEEE Transactions on Biomedical Engineering, PP(99), 2016.   
Z. Chen, R. Cabrera-Lozoya, J. Relan, M. Sohal, A. Shetty, R. Karim, H. Delingette, J. Gill, K. Rhode,N. Ayache, P. Taggart, C. A. Rinaldi, M. Sermesant, R. 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.   
S. Marchesseau, H. Delingette, M. Sermesant, R. Cabrera Lozoya, C. Tobon-Gomez, P. Moireau, R. M. Figueras I Ventura, K. Lekadir, A. Hernandez, M. Garreau, E. Donal, C. Leclercq, S. G. Duckett, K. Rhode, C. A. Rinaldi, A. F. Frangi, R. Razavi, D. Chapelle, N. 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.   
M. Sermesant, R. Chabiniok, P. Chinchapatnam, T. Mansi, F. Billet, P. Moireau, J.-M. Peyrat, K. C. L. Wong, J. Relan, K. Rhode, M. Ginks, P. Lambiase, H. Delingette, M. Sorine, C. A. Rinaldi, D. Chapelle, R. Razavi, N. 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

R. Molléro, X. Pennec, H. Delingette, A. Garny, N. Ayache, M. Sermesant. Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models. Biomechanics and Modeling in Mechanobiology, pp 1-16, September 2017.  
R. Chabiniok, V. Y. Wang, M. Hadjicharalambous, L. Asner, J. Lee, M. Sermesant, E. Kuhl, A. A. Young, P. Moireau, M. P. Nash, D. Chapelle, D. Nordsletten. Multiphysics and multiscale modelling, data-model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics. Interface Focus, 6(2), 2016.   
S. Marchesseau, H. Delingette, M. Sermesant, M. Sorine, K. Rhode, S. G. Duckett, C. A. Rinaldi, R. Razavi, N. 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.   
E. Konukoglu, J. Relan, U. Cilingir, B. Menze, P. Chinchapatnam, A. Jadidi, H. Cochet, M. Hocini, H. Delingette, P. Jaïs, M. Haïssaguerre, N. Ayache, 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.   
J. Relan, M. Pop, H. Delingette, G. Wright, N. Ayache, M. 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

K. Mcleod, M. Sermesant, P. Beerbaum, X. 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.   
Romain Guibert, K. Mcleod, A. Caiazzo, T. Mansi, M. A. Fernández, M. Sermesant, X. Pennec, I. Vignon-Clementel, Y. Boudjemline, J.-F. 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.   
N. Toussaint, C. Stoeck, T. Schaeffter, S. Kozerke, M. Sermesant, P. G. Batchelor. In vivo human cardiac fibre architecture estimation using shape-based diffusion tensor processing. Medical Image Analysis, 2013.
J. L. Bruse, A. Khushnood, K. Mcleod, G. Biglino, M. Sermesant, X. Pennec, A. M. Taylor, T.-Y. Hsia, S. 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

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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