H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 42 Citations 8,435 205 World Ranking 4024 National Ranking 52

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Internal medicine
  • Computer vision

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Segmentation, Image processing and Mass cytometry. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition. He has included themes like Magnetic resonance imaging and Atlas in his Computer vision study.

His work on Image segmentation as part of general Segmentation study is frequently connected to Iterative closest point, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His work carried out in the field of Image processing brings together such families of science as Orientation, Ground truth, Pose and Silhouette. Disease, Intestinal Disorder and Gastrointestinal tract is closely connected to Immune system in his research, which is encompassed under the umbrella topic of Mass cytometry.

His most cited work include:

  • Conserved cell types with divergent features in human versus mouse cortex (406 citations)
  • Conserved cell types with divergent features in human versus mouse cortex (406 citations)
  • 3-D active appearance models: segmentation of cardiac MR and ultrasound images (374 citations)

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

Boudewijn P. F. Lelieveldt mainly focuses on Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Magnetic resonance imaging. His work is connected to Active appearance model, Image registration, Image processing, Image segmentation and Principal component analysis, as a part of Artificial intelligence. His Image registration research is multidisciplinary, relying on both Transformation and Nuclear medicine.

His Computer vision study combines topics in areas such as Gradient descent, Atlas, Robustness and Cardiac cycle. His work on Active shape model as part of general Segmentation study is frequently linked to Initialization, bridging the gap between disciplines. His study looks at the relationship between Radiology and topics such as Coronary arteries, which overlap with Coronary artery disease.

He most often published in these fields:

  • Artificial intelligence (55.38%)
  • Computer vision (37.37%)
  • Segmentation (23.92%)

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

  • Mass cytometry (12.37%)
  • Artificial intelligence (55.38%)
  • Immune system (7.53%)

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

His primary scientific interests are in Mass cytometry, Artificial intelligence, Immune system, Neuroscience and Image registration. His Mass cytometry research includes elements of Cell, CD8, Data mining and Cell biology. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.

His studies in Neuroscience integrate themes in fields like Transcriptome, Cell type and Single-cell analysis. His Single-cell analysis study integrates concerns from other disciplines, such as Cerebral cortex, Middle temporal gyrus, Cortex and Excitatory postsynaptic potential. Boudewijn P. F. Lelieveldt combines subjects such as Decoding methods, Prostate, Nuclear medicine and Regression with his study of Image registration.

Between 2018 and 2021, his most popular works were:

  • Conserved cell types with divergent features in human versus mouse cortex (406 citations)
  • Conserved cell types with divergent features in human versus mouse cortex (406 citations)
  • Eleven grand challenges in single-cell data science (187 citations)

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

  • Artificial intelligence
  • Internal medicine
  • Cancer

Boudewijn P. F. Lelieveldt focuses on Mass cytometry, Immune system, Neuroscience, Cell type and Human brain. Boudewijn P. F. Lelieveldt interconnects Data mining, T cell, Antigen-presenting cell, Cell division and Cell biology in the investigation of issues within Mass cytometry. His Neuroscience study incorporates themes from Transcriptome and Single-cell analysis.

His biological study spans a wide range of topics, including Cerebral cortex, Middle temporal gyrus, Cortex and Excitatory postsynaptic potential. The study incorporates disciplines such as Neocortex, Epigenomics, Probabilistic logic, Gene and Epigenome in addition to Cell type. His work deals with themes such as BAP1 and Disease, Parkinson's disease, which intersect with Human brain.

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

3-D active appearance models: segmentation of cardiac MR and ultrasound images

S.C. Mitchell;J.G. Bosch;B.P.F. Lelieveldt;R.J. van der Geest.
IEEE Transactions on Medical Imaging (2002)

523 Citations

Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images

S.C. Mitchell;B.P.F. Lelieveldt;R.J. van der Geest;H.G. Bosch.
IEEE Transactions on Medical Imaging (2001)

441 Citations

Conserved cell types with divergent features in human versus mouse cortex

Rebecca D. Hodge;Trygve E. Bakken;Jeremy A. Miller;Kimberly A. Smith.
Nature (2019)

406 Citations

Automatic segmentation of echocardiographic sequences by active appearance motion models

J.G. Bosch;S.C. Mitchell;B.P.F. Lelieveldt;F. Nijland.
IEEE Transactions on Medical Imaging (2002)

378 Citations

Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease.

Denis P Shamonin;Esther E Bron;Boudewijn P.F. Lelieveldt;Marion Smits.
Frontiers in Neuroinformatics (2013)

357 Citations

SPASM: A 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data

Hans C. van Assen;Mikhail G. Danilouchkine;Alejandro F. Frangi;Sebastián Ordás.
Medical Image Analysis (2006)

258 Citations

An objective comparison of cell-tracking algorithms

Vladimír Ulman;Martin Maška;Klas E G Magnusson;Olaf Ronneberger.
Nature Methods (2017)

251 Citations

Nonrigid image registration using multi-scale 3D convolutional neural networks

Hessam Sokooti;Bob D. de Vos;Floris F. Berendsen;Boudewijn P. F. Lelieveldt;Boudewijn P. F. Lelieveldt.
medical image computing and computer assisted intervention (2017)

205 Citations

Eleven grand challenges in single-cell data science

David Lähnemann;David Lähnemann;Johannes Köster;Johannes Köster;Ewa Szczurek;Davis J. McCarthy;Davis J. McCarthy.
Genome Biology (2020)

187 Citations

Approximated and User Steerable tSNE for Progressive Visual Analytics

Nicola Pezzotti;Boudewijn P. F. Lelieveldt;Laurens van der Maaten;Thomas Hollt.
IEEE Transactions on Visualization and Computer Graphics (2017)

172 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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