D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 36 Citations 5,981 156 World Ranking 3142 National Ranking 66

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Internal medicine
  • Computer vision

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.

His most cited work include:

  • Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (323 citations)
  • Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation (297 citations)
  • Functional Imaging and Modeling of the Heart (211 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (34.47%)
  • Cardiology (22.18%)
  • Internal medicine (22.18%)

What were the highlights of his more recent work (between 2017-2021)?

  • Cardiology (22.18%)
  • Internal medicine (22.18%)
  • Artificial intelligence (34.47%)

In recent papers he was focusing on the following fields of study:

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.

Between 2017 and 2021, his most popular works were:

  • Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (323 citations)
  • Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge (40 citations)
  • Three-dimensional right-ventricular regional deformation and survival in pulmonary hypertension. (37 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Internal medicine
  • Computer vision

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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)

369 Citations

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)

365 Citations

An electromechanical model of the heart for image analysis and simulation

M. Sermesant;H. Delingette;N. Ayache.
IEEE Transactions on Medical Imaging (2006)

263 Citations

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)

228 Citations

Functional Imaging and Modeling of the Heart

Nicholas Ayache;Hervé Delingette;Maxime Sermesant.
Lecture Notes in Computer Science (2009)

213 Citations

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)

208 Citations

A system for real-time XMR guided cardiovascular intervention

K.S. Rhode;M. Sermesant;D. Brogan;S. Hegde.
IEEE Transactions on Medical Imaging (2005)

206 Citations

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)

187 Citations

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)

186 Citations

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)

168 Citations

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Best Scientists Citing Maxime Sermesant

Nicholas Ayache

Nicholas Ayache

French Institute for Research in Computer Science and Automation - INRIA

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Alejandro F. Frangi

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Dorin Comaniciu

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Andrew P. King

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