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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
The set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences are reported, finding that different algorithms worked best for different sub-regions, but that no single algorithm ranked in the top for all sub-Regions simultaneously.
A Riemannian Framework for Tensor Computing
- X. Pennec, P. Fillard, N. Ayache
- Computer Science, MathematicsInternational Journal of Computer Vision
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.
Log‐Euclidean metrics for fast and simple calculus on diffusion tensors
- V. Arsigny, P. Fillard, X. Pennec, N. Ayache
- Computer ScienceMagnetic resonance in medicine
- 1 August 2006
A new family of Riemannian metrics called Log‐Euclidean is proposed, based on a novel vector space structure for tensors, which can be converted into Euclidean ones once tensors have been transformed into their matrix logarithms.
Diffeomorphic demons: Efficient non-parametric image registration
Geometric Means in a Novel Vector Space Structure on Symmetric Positive-Definite Matrices
This work defines the Log‐Euclidean mean from a Riemannian point of view, based on a lie group structure which is compatible with the usual algebraic properties of this matrix space and a new scalar multiplication that smoothly extends the Lie group structure into a vector space structure.
Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach
- Tom Kamiel Magda Vercauteren, X. Pennec, A. Perchant, N. Ayache
- Computer Science, MathematicsMICCAI
- 6 September 2008
This work proposes a non-linear registration algorithm perfectly fit for log-Euclidean statistics on diffeomorphisms that outperforms both the demons algorithm and the recently proposed diffeomorphic demons algorithm in terms of accuracy of the transformation while remaining computationally efficient.
A Log-Euclidean Framework for Statistics on Diffeomorphisms
This article focuses on the computation of statistics of invertible geometrical deformations (i.e., diffeomorphisms), based on the generalization to this type of data of the notion of principal logarithm, which is a simple 3D vector field and well-defined for diffe morphisms close enough to the identity.
Model-Based Detection of Tubular Structures in 3D Images
- K. Krissian, G. Malandain, N. Ayache, Régis Vaillant, Y. Trousset
- MathematicsComput. Vis. Image Underst.
- 1 November 2000
A new approach for centerline detection and reconstruction of 3D tubular structures and a multiscale analysis for extracting vessels of different sizes according to the scale is presented.
Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration
- B. Yeo, M. Sabuncu, Tom Kamiel Magda Vercauteren, N. Ayache, B. Fischl, P. Golland
- Computer ScienceIEEE Transactions on Medical Imaging
- 1 March 2010
The Spherical Demons algorithm for registering two spherical images is presented and a large class of regularizors for the modified Demons objective function can be efficiently approximated on the sphere using iterative smoothing and the resulting registration is diffeomorphic and fast.