Maxime Sermesant

My research work combines cardiac modelling and 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. Moreover there is an important underlying software aspect in order to achieve a clinical impact.


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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