M
Marius Staring
Researcher at Leiden University Medical Center
Publications - 132
Citations - 10612
Marius Staring is an academic researcher from Leiden University Medical Center. The author has contributed to research in topics: Image registration & Computer science. The author has an hindex of 32, co-authored 117 publications receiving 8672 citations. Previous affiliations of Marius Staring include Utrecht University & Loyola University Medical Center.
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Journal ArticleDOI
elastix : A Toolbox for Intensity-Based Medical Image Registration
TL;DR: The software consists of a collection of algorithms that are commonly used to solve medical image registration problems, and allows the user to quickly configure, test, and compare different registration methods for a specific application.
Journal ArticleDOI
A deep learning framework for unsupervised affine and deformable image registration
Bob D. de Vos,Floris F. Berendsen,Max A. Viergever,Hessam Sokooti,Marius Staring,Ivana Išgum +5 more
TL;DR: In this paper, the Deep Learning Image Registration (DLIR) framework is proposed for unsupervised affine and deformable image registration, where CNNs are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration.
Journal ArticleDOI
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,Stefan Klein,Marius Staring +5 more
TL;DR: The accelerated registration tool elastix is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI and has nearly identical results to the non-optimized version.
Journal ArticleDOI
Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using Mutual Information and B-Splines
TL;DR: This work compares the performance of eight optimization methods: gradient descent, quasi-Newton, nonlinear conjugate gradient, Kiefer-Wolfowitz, simultaneous perturbation, Robbins-Monro, and evolution strategy, and shows that the Robbins- Monro method is the best choice in most applications.
Journal ArticleDOI
Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge
Keelin Murphy,B. van Ginneken,Joseph M. Reinhardt,Sven Kabus,Kai Ding,Xiang Deng,Kunlin Cao,Kaifang Du,Gary E. Christensen,V. Garcia,Tom Vercauteren,Nicholas Ayache,Olivier Commowick,Grégoire Malandain,Ben Glocker,Nikos Paragios,Nassir Navab,Vladlena Gorbunova,Jon Sporring,M. de Bruijne,Xiao Han,Mattias P. Heinrich,Julia A. Schnabel,Mark Jenkinson,Cristian Lorenz,Marc Modat,Jamie R. McClelland,Sebastien Ourselin,Sascha E. A. Muenzing,Max A. Viergever,Dante De Nigris,D. L. Collins,Tal Arbel,M. Peroni,Rui Li,Gregory C. Sharp,Alexander Schmidt-Richberg,Jan Ehrhardt,René Werner,Dirk Smeets,Dirk Loeckx,Gang Song,Nicholas J. Tustison,Brian B. Avants,James C. Gee,Marius Staring,Stefan Klein,Berend C. Stoel,Martin Urschler,Manuel Werlberger,Jef Vandemeulebroucke,Simon Rit,David Sarrut,Josien P. W. Pluim +53 more
TL;DR: The organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups are detailed.