H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 41 Citations 8,429 208 World Ranking 4363 National Ranking 260

Research.com Recognitions

Awards & Achievements

2021 - IEEE Fellow For contributions to medical image computing

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Computer vision
  • Magnetic resonance imaging

Her primary areas of investigation include Artificial intelligence, Computer vision, Image registration, Segmentation and Algorithm. Her work deals with themes such as Real-time MRI and Pattern recognition, which intersect with Artificial intelligence. Her Computer vision study combines topics in areas such as Magnetic resonance imaging, Mutual information and Atlas.

Her Image registration research incorporates elements of Image segmentation, Medical imaging, Mammography, Boundary value problem and Feature extraction. Julia A. Schnabel usually deals with Segmentation and limits it to topics linked to Active appearance model and Brain atlas, Sørensen–Dice coefficient and Standard deviation. Her Algorithm study incorporates themes from White matter, Brain segmentation, Cerebrospinal fluid and Image warping.

Her most cited work include:

  • Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration (404 citations)
  • MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration (352 citations)
  • Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling (347 citations)

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

Julia A. Schnabel spends much of her time researching Artificial intelligence, Computer vision, Image registration, Pattern recognition and Segmentation. Her research combines Magnetic resonance imaging and Artificial intelligence. Her studies link Mutual information with Computer vision.

The various areas that Julia A. Schnabel examines in her Image registration study include Motion estimation, Regularization, Algorithm, Mammography and Voxel. Her Pattern recognition research includes elements of Image and Feature. Julia A. Schnabel has included themes like Spatial analysis and Ultrasound in her Segmentation study.

She most often published in these fields:

  • Artificial intelligence (70.85%)
  • Computer vision (46.78%)
  • Image registration (31.19%)

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

  • Artificial intelligence (70.85%)
  • Deep learning (10.85%)
  • Computer vision (46.78%)

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

Julia A. Schnabel mainly investigates Artificial intelligence, Deep learning, Computer vision, Segmentation and Pattern recognition. Many of her studies involve connections with topics such as Ultrasound and Artificial intelligence. Her work on Image quality as part of general Computer vision study is frequently linked to Position, therefore connecting diverse disciplines of science.

Her Segmentation research includes themes of Cardiology, Modality, Perfusion scanning and Uterus. Her Pattern recognition study combines topics from a wide range of disciplines, such as Spatial analysis, Medical imaging, Magnetic resonance imaging, Voxel and Partial volume. Her Voxel research focuses on subjects like Ventilation, which are linked to Image registration.

Between 2018 and 2021, her most popular works were:

  • Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function. (43 citations)
  • Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning. (29 citations)
  • A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology. (24 citations)

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

  • Artificial intelligence
  • Magnetic resonance imaging
  • Computer vision

Her main research concerns Artificial intelligence, Deep learning, Segmentation, Pattern recognition and Convolutional neural network. The study incorporates disciplines such as Machine learning, Ultrasound and Computer vision in addition to Artificial intelligence. Her Ultrasound research is multidisciplinary, incorporating perspectives in Image registration, Brain shift, Brain tumor surgery and Benchmark.

Her work on Iterative reconstruction as part of general Computer vision research is frequently linked to Multi photon microscopy, thereby connecting diverse disciplines of science. She combines subjects such as Modality, Esophagus, Algorithm, Key and Endoscopy with her study of Segmentation. Her work in Pattern recognition addresses issues such as Magnetic resonance imaging, which are connected to fields such as Parametric statistics, Matching, Recurrent neural network and Left ventricular myocardium.

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

Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration

D. Rueckert;A.F. Frangi;J.A. Schnabel.
IEEE Transactions on Medical Imaging (2003)

540 Citations

Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling

A.F. Frangi;D. Rueckert;J.A. Schnabel;W.J. Niessen.
IEEE Transactions on Medical Imaging (2002)

495 Citations

A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations

Julia A. Schnabel;Daniel Rueckert;Marcel Quist;Jane M. Blackall.
medical image computing and computer assisted intervention (2001)

487 Citations

MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

Mattias P. Heinrich;Mark Jenkinson;Manav Bhushan;Tahreema Matin.
Medical Image Analysis (2012)

463 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

Validation of nonrigid image registration using finite-element methods: application to breast MR images

J.A. Schnabel;C. Tanner;A.D. Castellano-Smith;A. Degenhard.
IEEE Transactions on Medical Imaging (2003)

341 Citations

Non-local shape descriptor: a new similarity metric for deformable multi-modal registration

Mattias P. Heinrich;Mark Jenkinson;Manav Bhushan;Tahreema Matin.
medical image computing and computer assisted intervention (2011)

262 Citations

An evaluation of four automatic methods of segmenting the subcortical structures in the brain.

Kolawole Oluwole Babalola;Brian Patenaude;Paul Aljabar;Julia A. Schnabel.
NeuroImage (2009)

224 Citations

Reconstruction of fetal brain MRI with intensity matching and complete outlier removal.

Maria Kuklisova-Murgasova;Gerardine Quaghebeur;Mary A. Rutherford;Joseph V. Hajnal.
Medical Image Analysis (2012)

221 Citations

Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration

Daniel Rueckert;Alejandro F. Frangi;Alejandro F. Frangi;Julia A. Schnabel.
medical image computing and computer assisted intervention (2001)

219 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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