H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 78 Citations 24,672 588 World Ranking 489 National Ranking 30
Medicine H-index 87 Citations 27,744 606 World Ranking 6758 National Ranking 629

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Internal medicine
  • Magnetic resonance imaging

His primary areas of study are Artificial intelligence, Segmentation, Magnetic resonance imaging, Pathology and Computer vision. His Artificial intelligence research incorporates elements of Machine learning and Pattern recognition. Sebastien Ourselin focuses mostly in the field of Segmentation, narrowing it down to matters related to Data mining and, in some cases, Categorical variable.

His Magnetic resonance imaging research is multidisciplinary, incorporating elements of Positron emission tomography, Nuclear medicine, Neuroimaging and Brain mapping. The various areas that Sebastien Ourselin examines in his Pathology study include Voxel-based morphometry and Neuroscience. His Neuroscience research incorporates themes from White matter and Disease.

His most cited work include:

  • Fast free-form deformation using graphics processing units (674 citations)
  • Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations (525 citations)
  • Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations (525 citations)

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

His main research concerns Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Magnetic resonance imaging. Many of his studies on Artificial intelligence apply to Machine learning as well. His Computer vision research includes themes of Imaging phantom, Robustness and Medical imaging.

Sebastien Ourselin mostly deals with Scale-space segmentation in his studies of Segmentation. Sebastien Ourselin interconnects Positron emission tomography, Nuclear medicine, Atrophy, Pathology and Neuroimaging in the investigation of issues within Magnetic resonance imaging. His studies deal with areas such as Alzheimer's disease and Frontotemporal dementia as well as Atrophy.

He most often published in these fields:

  • Artificial intelligence (38.05%)
  • Computer vision (18.61%)
  • Segmentation (19.14%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (38.05%)
  • Pattern recognition (16.34%)
  • Segmentation (19.14%)

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

Sebastien Ourselin focuses on Artificial intelligence, Pattern recognition, Segmentation, Convolutional neural network and Magnetic resonance imaging. His research integrates issues of Machine learning and Computer vision in his study of Artificial intelligence. Sebastien Ourselin has researched Pattern recognition in several fields, including Domain, Modality, Neuroimaging and Medical imaging.

His Magnetic resonance imaging research is under the purview of Radiology.

Between 2018 and 2021, his most popular works were:

  • Real-time tracking of self-reported symptoms to predict potential COVID-19. (455 citations)
  • Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. (394 citations)
  • Rapid implementation of mobile technology for real-time epidemiology of COVID-19. (137 citations)

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

  • Artificial intelligence
  • Internal medicine
  • Statistics

His primary areas of study are Artificial intelligence, Segmentation, Pattern recognition, Disease and Internal medicine. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Computer vision. His Segmentation research focuses on Overfitting and how it relates to Algorithm and Set.

Sebastien Ourselin does research in Pattern recognition, focusing on Image segmentation specifically. His Frontotemporal dementia study in the realm of Disease interacts with subjects such as Anosmia. His study in Convolutional neural network is interdisciplinary in nature, drawing from both Magnetic resonance imaging and Dice.

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.

Top Publications

Fast free-form deformation using graphics processing units

Marc Modat;Gerard R. Ridgway;Zeike A. Taylor;Manja Lehmann.
Computer Methods and Programs in Biomedicine (2010)

813 Citations

Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

Carole H. Sudre;Carole H. Sudre;Wenqi Li;Tom Vercauteren;Sebastien Ourselin;Sebastien Ourselin.
3rd MICCAI International Workshop on Deep Learning in Medical Image Analysis (DLMIA) / 7th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS), Date: 2017/09/14, Location: Quebec, CANADA (2017)

692 Citations

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer.
Unknown Journal (2018)

685 Citations

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

Nicholas Ayache;Sébastien Ourselin;Anthony Maeder.
(2007)

660 Citations

Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study.

Long H. Nguyen;David A. Drew;Mark S. Graham;Amit D. Joshi.
The Lancet. Public health (2020)

595 Citations

Reconstructing a 3D structure from serial histological sections

Sébastien Ourselin;Alexis Roche;Gérard Subsol;Xavier Pennec.
Image and Vision Computing (2001)

576 Citations

Real-time tracking of self-reported symptoms to predict potential COVID-19.

Cristina Menni;Ana M Valdes;Ana M Valdes;Maxim B Freidin;Carole H Sudre.
Nature Medicine (2020)

552 Citations

Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

K. Murphy;B. van Ginneken;J. M. Reinhardt;S. Kabus.
IEEE Transactions on Medical Imaging (2011)

401 Citations

NiftyNet: a deep-learning platform for medical imaging

Eli Gibson;Wenqi Li;Carole H. Sudre;Lucas Fidon.
Computer Methods and Programs in Biomedicine (2018)

373 Citations

Head size, age and gender adjustment in MRI studies: a necessary nuisance?

Josephine Barnes;Gerard R. Ridgway;Gerard R. Ridgway;Jonathan W. Bartlett;Susie M. D. Henley.
NeuroImage (2010)

372 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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