Journal Article10.1007/3-540-48714-x_12
Automatic Detection and Segmentation of Evolving Processes in 3D Medical Images: Application to Multiple Sclerosis
David Rey,G. Subsol,Hervé Delingette,N. Ayache +3 more
- 28 Jun 1999
Vol. 6, Iss: 2, pp 163-79
189
TL;DR: The objective of this work was to automatically detect regions with apparent local volume variation with a vector field operator applied to the local displacement field obtained after a non-rigid registration between two successive temporal images.
read more
About: The article was published on 28 Jun 1999.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Medical image analysis: progress over two decades and the challenges ahead
James S. Duncan,Nicholas Ayache +1 more
TL;DR: A look at progress in the field over the last 20 years is looked at and some of the challenges that remain for the years to come are suggested.
4.3K
A Riemannian Framework for Tensor Computing
TL;DR: This paper proposes to endow the tensor space with an affine-invariant Riemannian metric and demonstrates that it leads to strong theoretical properties: the cone of positive definite symmetric matrices is replaced by a regular and complete manifold without boundaries, the geodesic between two tensors and the mean of a set of tensors are uniquely defined.
Automated segmentation of multiple sclerosis lesions by model outlier detection
TL;DR: A fully automated algorithm for segmentation of multiple sclerosis lesions from multispectral magnetic resonance (MR) images that performs intensity-based tissue classification using a stochastic model and simultaneously detects MS lesions as outliers that are not well explained by the model.
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.
Understanding the Demon's Algorithm: 3D Non-rigid Registration by Gradient Descent
Xavier Pennec,Pascal Cachier,Nicholas Ayache +2 more
- 19 Sep 1999
TL;DR: This paper shows that the "Demons Algorithm" can be considered as anroximation of a second order gradient descent on the sum of square of intensity differences criterion and reformulate Gaussian and physical model regulariza- tions as minimization problems.
References
Multimodality image registration by maximization of mutual information
TL;DR: The results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.
A survey of image registration techniques
TL;DR: This paper organizes this material by establishing the relationship between the variations in the images and the type of registration techniques which can most appropriately be applied, and establishing a framework for understanding the merits and relationships between the wide variety of existing techniques.
5K
Image matching as a diffusion process: an analogy with Maxwell's demons
TL;DR: The main idea is to consider the objects boundaries in one image as semi-permeable membranes and to let the other image, considered as a deformable grid model, diffuse through these interfaces, by the action of effectors situated within the membranes.
2.6K
Finite element implementation of incompressible, transversely isotropic hyperelasticity
TL;DR: In this article, a three-dimensional constitutive model for biological soft tissues and its finite element implementation for fully incompressible material behavior is presented, along with derivations of the stress and elasticity tensors for a transversely isotropic, hyperelastic material.
834
Unifying Maximum Likelihood Approaches in Medical Image Registration
TL;DR: This paper clarifies the assumptions on which a number of popular similarity measures rely and shows that the search for an optimal measure can be cast into a maximum likelihood estimation problem.