H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 50 Citations 17,714 210 World Ranking 2875 National Ranking 166

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Surgery
  • Internal medicine

Artificial intelligence, Segmentation, Pattern recognition, Image registration and Convolutional neural network are his primary areas of study. His research integrates issues of Machine learning and Computer vision in his study of Artificial intelligence. His Segmentation study incorporates themes from Magnetic resonance imaging, Brain tumor and Deep learning.

Tom Vercauteren interconnects Algorithm and Medical imaging in the investigation of issues within Image registration. His Algorithm research includes themes of Vector field, Diffeomorphism, Mathematical optimization and Pattern recognition. Tom Vercauteren has researched Pattern recognition in several fields, including Normalization, Atlas and Neuroimaging.

His most cited work include:

  • Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. (1732 citations)
  • Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. (1732 citations)
  • Diffeomorphic demons: efficient non-parametric image registration. (945 citations)

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

His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Convolutional neural network. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Endomicroscopy. His work on Image as part of general Computer vision research is often related to Fetoscopy, thus linking different fields of science.

His studies deal with areas such as Artificial neural network, Similarity and Feature as well as Pattern recognition. Many of his studies on Segmentation involve topics that are commonly interrelated, such as Magnetic resonance imaging. Tom Vercauteren combines subjects such as Algorithm and Medical imaging with his study of Image registration.

He most often published in these fields:

  • Artificial intelligence (61.79%)
  • Computer vision (25.71%)
  • Pattern recognition (27.36%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (61.79%)
  • Pattern recognition (27.36%)
  • Segmentation (26.89%)

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

Tom Vercauteren mostly deals with Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Convolutional neural network. His study looks at the relationship between Artificial intelligence and fields such as Computer vision, as well as how they intersect with chemical problems. His work carried out in the field of Pattern recognition brings together such families of science as Similarity, Frame, Bayesian inference, Benchmark and Prostate cancer.

His Segmentation research is multidisciplinary, incorporating perspectives in Contrast, Magnetic resonance imaging and Vestibular system. His Deep learning study integrates concerns from other disciplines, such as Empirical risk minimization, Artificial neural network, Superresolution, Visualization and Robustness. Tom Vercauteren has included themes like Process, Margin, Gold standard, Ground truth and Feature extraction in his Convolutional neural network study.

Between 2019 and 2021, his most popular works were:

  • An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. (28 citations)
  • An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. (28 citations)
  • An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. (28 citations)

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

  • Artificial intelligence
  • Surgery
  • Internal medicine

His primary scientific interests are in Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Convolutional neural network. His Artificial intelligence research incorporates elements of Residual and Computer vision. His work on Principal component analysis as part of general Pattern recognition research is frequently linked to Shrinkage estimator, bridging the gap between disciplines.

The Deep learning study combines topics in areas such as Text mining, Lung lesion, Radiology and Automatic segmentation. Tom Vercauteren brings together Segmentation and Dice to produce work in his papers. His Convolutional neural network research includes elements of Ground truth, Feature extraction and Software.

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

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

Arno Klein;Jesper L. R. Andersson;Babak A. Ardekani;Babak A. Ardekani;John Ashburner.
NeuroImage (2009)

2024 Citations

Diffeomorphic demons: efficient non-parametric image registration.

Tom Vercauteren;Xavier Pennec;Aymeric Perchant;Nicholas Ayache.
NeuroImage (2009)

1247 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

Non-parametric diffeomorphic image registration with the demons algorithm

Tom Vercauteren;Xavier Pennec;Aymeric Perchant;Nicholas Ayache.
medical image computing and computer assisted intervention (2007)

428 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

Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach

Tom Vercauteren;Xavier Pennec;Aymeric Perchant;Nicholas Ayache.
medical image computing and computer assisted intervention (2008)

395 Citations

In Vivo Imaging of the Bronchial Wall Microstructure Using Fibered Confocal Fluorescence Microscopy

Luc Thiberville;Sophie Moreno-Swirc;Tom Vercauteren;Eric Peltier.
American Journal of Respiratory and Critical Care Medicine (2007)

377 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

Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration

B.T.T. Yeo;M.R. Sabuncu;T. Vercauteren;N. Ayache.
IEEE Transactions on Medical Imaging (2010)

324 Citations

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