Maxime Sermesant mainly focuses on Artificial intelligence, Computer vision, Magnetic resonance imaging, Medical imaging and Cardiology. The study incorporates disciplines such as Estimation theory, Machine learning and Personalization in addition to Artificial intelligence. His Computer vision research incorporates themes from 3D ultrasound and Biomechanical model.
His Magnetic resonance imaging study integrates concerns from other disciplines, such as Atlas and Catheterization procedure, Cardiac catheterization. His studies examine the connections between Medical imaging and genetics, as well as such issues in Ventricle, with regards to Effective treatment, Heart failure and Acute effects. He regularly ties together related areas like Internal medicine in his Cardiology studies.
Maxime Sermesant spends much of his time researching Artificial intelligence, Cardiology, Internal medicine, Computer vision and Cardiac electrophysiology. His work deals with themes such as Machine learning and Pattern recognition, which intersect with Artificial intelligence. Much of his study explores Cardiology relationship to Magnetic resonance imaging.
His Computer vision research is multidisciplinary, incorporating perspectives in 3D ultrasound, Ultrasound and Biomechanical model. His study in Cardiac electrophysiology is interdisciplinary in nature, drawing from both Optical imaging, Simulation, Biomedical engineering and Personalization. Maxime Sermesant combines subjects such as Estimation theory and Data mining with his study of Personalization.
Cardiology, Internal medicine, Artificial intelligence, Medical imaging and Pattern recognition are his primary areas of study. His study in the field of Rv function, Tetralogy of Fallot and Exercise stress echocardiography is also linked to topics like Outflow. His studies in Artificial intelligence integrate themes in fields like Machine learning, Computer vision and Cardiac electrophysiology.
In his study, Ventricular tachycardia is inextricably linked to Radiofrequency ablation, which falls within the broad field of Computer vision. His Medical imaging research includes themes of Segmentation, Image segmentation, Field, Myocardial infarction and Convolutional neural network. His studies deal with areas such as Pixel, Image registration and Resolution as well as Pattern recognition.
Maxime Sermesant mostly deals with Myocardial infarction, Magnetic resonance imaging, Artificial intelligence, Ventricular tachycardia and Medical imaging. Maxime Sermesant has researched Magnetic resonance imaging in several fields, including Modality and Segmentation. As part of the same scientific family, Maxime Sermesant usually focuses on Artificial intelligence, concentrating on Computer vision and intersecting with Ultrasound.
His research integrates issues of Ablation and Nuclear medicine in his study of Ventricular tachycardia. The concepts of his Medical imaging study are interwoven with issues in Feature extraction and Pattern recognition. The Image segmentation study which covers Deep learning that intersects with Ventricle.
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Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky.
IEEE Transactions on Medical Imaging (2018)
Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation
O. Clatz;M. Sermesant;P.-Y. Bondiau;H. Delingette.
IEEE Transactions on Medical Imaging (2005)
An electromechanical model of the heart for image analysis and simulation
M. Sermesant;H. Delingette;N. Ayache.
IEEE Transactions on Medical Imaging (2006)
Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation.
Maxime Sermesant;Maxime Sermesant;Radomir Chabiniok;Phani Pradeep Chinchapatnam;T. Mansi.
Medical Image Analysis (2012)
Functional Imaging and Modeling of the Heart
Nicholas Ayache;Hervé Delingette;Maxime Sermesant.
Lecture Notes in Computer Science (2009)
Inverse relationship between fractionated electrograms and atrial fibrosis in persistent atrial fibrillation: combined magnetic resonance imaging and high-density mapping.
Amir S. Jadidi;Hubert Cochet;Ashok J. Shah;Steven J. Kim.
Journal of the American College of Cardiology (2013)
A system for real-time XMR guided cardiovascular intervention
K.S. Rhode;M. Sermesant;D. Brogan;S. Hegde.
IEEE Transactions on Medical Imaging (2005)
Cardiac function estimation from MRI using a heart model and data assimilation: advances and difficulties.
Maxime Sermesant;Maxime Sermesant;Philippe Moireau;Oscar Camara;Jacques Sainte-Marie.
Medical Image Analysis (2006)
iLogDemons: A Demons-Based Registration Algorithm for Tracking Incompressible Elastic Biological Tissues
Tommaso Mansi;Xavier Pennec;Maxime Sermesant;Hervé Delingette.
International Journal of Computer Vision (2011)
SVF-Net: Learning Deformable Image Registration Using Shape Matching
Marc-Michel Rohé;Manasi Datar;Tobias Heimann;Maxime Sermesant.
medical image computing and computer assisted intervention (2017)
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