11,113 research outputs found
Deformable Registration through Learning of Context-Specific Metric Aggregation
We propose a novel weakly supervised discriminative algorithm for learning
context specific registration metrics as a linear combination of conventional
similarity measures. Conventional metrics have been extensively used over the
past two decades and therefore both their strengths and limitations are known.
The challenge is to find the optimal relative weighting (or parameters) of
different metrics forming the similarity measure of the registration algorithm.
Hand-tuning these parameters would result in sub optimal solutions and quickly
become infeasible as the number of metrics increases. Furthermore, such
hand-crafted combination can only happen at global scale (entire volume) and
therefore will not be able to account for the different tissue properties. We
propose a learning algorithm for estimating these parameters locally,
conditioned to the data semantic classes. The objective function of our
formulation is a special case of non-convex function, difference of convex
function, which we optimize using the concave convex procedure. As a proof of
concept, we show the impact of our approach on three challenging datasets for
different anatomical structures and modalities.Comment: Accepted for publication in the 8th International Workshop on Machine
Learning in Medical Imaging (MLMI 2017), in conjunction with MICCAI 201
Fast Predictive Multimodal Image Registration
We introduce a deep encoder-decoder architecture for image deformation
prediction from multimodal images. Specifically, we design an image-patch-based
deep network that jointly (i) learns an image similarity measure and (ii) the
relationship between image patches and deformation parameters. While our method
can be applied to general image registration formulations, we focus on the
Large Deformation Diffeomorphic Metric Mapping (LDDMM) registration model. By
predicting the initial momentum of the shooting formulation of LDDMM, we
preserve its mathematical properties and drastically reduce the computation
time, compared to optimization-based approaches. Furthermore, we create a
Bayesian probabilistic version of the network that allows evaluation of
registration uncertainty via sampling of the network at test time. We evaluate
our method on a 3D brain MRI dataset using both T1- and T2-weighted images. Our
experiments show that our method generates accurate predictions and that
learning the similarity measure leads to more consistent registrations than
relying on generic multimodal image similarity measures, such as mutual
information. Our approach is an order of magnitude faster than
optimization-based LDDMM.Comment: Accepted as a conference paper for ISBI 201
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of
accurate correspondences between points in 3D space. The correspondence problem
is generally addressed by the design of discriminative 3D local descriptors on
the one hand, and the development of robust matching strategies on the other
hand. In this work, we first propose a multi-view local descriptor, which is
learned from the images of multiple views, for the description of 3D keypoints.
Then, we develop a robust matching approach, aiming at rejecting outlier
matches based on the efficient inference via belief propagation on the defined
graphical model. We have demonstrated the boost of our approaches to
registration on the public scanning and multi-view stereo datasets. The
superior performance has been verified by the intensive comparisons against a
variety of descriptors and matching methods
Fast Predictive Image Registration
We present a method to predict image deformations based on patch-wise image
appearance. Specifically, we design a patch-based deep encoder-decoder network
which learns the pixel/voxel-wise mapping between image appearance and
registration parameters. Our approach can predict general deformation
parameterizations, however, we focus on the large deformation diffeomorphic
metric mapping (LDDMM) registration model. By predicting the LDDMM
momentum-parameterization we retain the desirable theoretical properties of
LDDMM, while reducing computation time by orders of magnitude: combined with
patch pruning, we achieve a 1500x/66x speed up compared to GPU-based
optimization for 2D/3D image registration. Our approach has better prediction
accuracy than predicting deformation or velocity fields and results in
diffeomorphic transformations. Additionally, we create a Bayesian probabilistic
version of our network, which allows evaluation of deformation field
uncertainty through Monte Carlo sampling using dropout at test time. We show
that deformation uncertainty highlights areas of ambiguous deformations. We
test our method on the OASIS brain image dataset in 2D and 3D
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
This paper introduces Quicksilver, a fast deformable image registration
method. Quicksilver registration for image-pairs works by patch-wise prediction
of a deformation model based directly on image appearance. A deep
encoder-decoder network is used as the prediction model. While the prediction
strategy is general, we focus on predictions for the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the
momentum-parameterization of LDDMM, which facilitates a patch-wise prediction
strategy while maintaining the theoretical properties of LDDMM, such as
guaranteed diffeomorphic mappings for sufficiently strong regularization. We
also provide a probabilistic version of our prediction network which can be
sampled during the testing time to calculate uncertainties in the predicted
deformations. Finally, we introduce a new correction network which greatly
increases the prediction accuracy of an already existing prediction network. We
show experimental results for uni-modal atlas-to-image as well as uni- / multi-
modal image-to-image registrations. These experiments demonstrate that our
method accurately predicts registrations obtained by numerical optimization, is
very fast, achieves state-of-the-art registration results on four standard
validation datasets, and can jointly learn an image similarity measure.
Quicksilver is freely available as an open-source software.Comment: Add new discussion
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