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Deep Group-wise Variational Diffeomorphic Image Registration
Deep neural networks are increasingly used for pair-wise image registrat...
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A matrix-free approach to parallel and memory-efficient deformable image registration
We present a novel computational approach to fast and memory-efficient d...
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Computing Topology Preservation of RBF Transformations for Landmark-Based Image Registration
In image registration, a proper transformation should be topology preser...
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Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors
In this paper, we present supervision-by-registration, an unsupervised a...
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A Generalized Framework for Analytic Regularization of Uniform Cubic B-spline Displacement Fields
Image registration is an inherently ill-posed problem that lacks the con...
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BW - Eye Ophthalmologic decision support system based on clinical workflow and data mining techniques-image registration algorithm
Blueworks - Medical Expert Diagnosis is developing an application, BWEye...
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A landmark-based algorithm for automatic pattern recognition and abnormality detection
We study a class of mathematical and statistical algorithms with the aim...
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Fast geodesic shooting for landmark matching using CUDA
Landmark matching via geodesic shooting is a prerequisite task for numerous registration based applications in biomedicine. Geodesic shooting has been developed as one solution approach and formulates the diffeomorphic registration as an optimal control problem under the Hamiltonian framework. In this framework, with landmark positions q0 fixed, the problem solely depends on the initial momentum p0 and evolves through time steps according to a set of constraint equations. Given an initial p0, the algorithm flows q and p forward through time steps, calculates a loss based on point-set mismatch and kinetic energy, back-propagate through time to calculate gradient on p0 and update it. In the forward and backward pass, a pair-wise kernel on landmark points K and additional intermediate terms have to be calculated and marginalized, leading to O(N2) computational complexity, N being the number of points to be registered. For medical image applications, N maybe in the range of thousands, rendering this operation computationally expensive. In this work we ropose a CUDA implementation based on shared memory reduction. Our implementation achieves nearly 2 orders magnitude speed up compared to a naive CPU-based implementation, in addition to improved numerical accuracy as well as better registration results.
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