6,458 research outputs found
An Unsupervised Learning Model for Deformable Medical Image Registration
We present a fast learning-based algorithm for deformable, pairwise 3D
medical image registration. Current registration methods optimize an objective
function independently for each pair of images, which can be time-consuming for
large data. We define registration as a parametric function, and optimize its
parameters given a set of images from a collection of interest. Given a new
pair of scans, we can quickly compute a registration field by directly
evaluating the function using the learned parameters. We model this function
using a convolutional neural network (CNN), and use a spatial transform layer
to reconstruct one image from another while imposing smoothness constraints on
the registration field. The proposed method does not require supervised
information such as ground truth registration fields or anatomical landmarks.
We demonstrate registration accuracy comparable to state-of-the-art 3D image
registration, while operating orders of magnitude faster in practice. Our
method promises to significantly speed up medical image analysis and processing
pipelines, while facilitating novel directions in learning-based registration
and its applications. Our code is available at
https://github.com/balakg/voxelmorph .Comment: 9 pages, in CVPR 201
An Efficient Cell List Implementation for Monte Carlo Simulation on GPUs
Maximizing the performance potential of the modern day GPU architecture
requires judicious utilization of available parallel resources. Although
dramatic reductions can often be obtained through straightforward mappings,
further performance improvements often require algorithmic redesigns to more
closely exploit the target architecture. In this paper, we focus on efficient
molecular simulations for the GPU and propose a novel cell list algorithm that
better utilizes its parallel resources. Our goal is an efficient GPU
implementation of large-scale Monte Carlo simulations for the grand canonical
ensemble. This is a particularly challenging application because there is
inherently less computation and parallelism than in similar applications with
molecular dynamics. Consistent with the results of prior researchers, our
simulation results show traditional cell list implementations for Monte Carlo
simulations of molecular systems offer effectively no performance improvement
for small systems [5, 14], even when porting to the GPU. However for larger
systems, the cell list implementation offers significant gains in performance.
Furthermore, our novel cell list approach results in better performance for all
problem sizes when compared with other GPU implementations with or without cell
lists.Comment: 30 page
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
Highly accelerated simulations of glassy dynamics using GPUs: caveats on limited floating-point precision
Modern graphics processing units (GPUs) provide impressive computing
resources, which can be accessed conveniently through the CUDA programming
interface. We describe how GPUs can be used to considerably speed up molecular
dynamics (MD) simulations for system sizes ranging up to about 1 million
particles. Particular emphasis is put on the numerical long-time stability in
terms of energy and momentum conservation, and caveats on limited
floating-point precision are issued. Strict energy conservation over 10^8 MD
steps is obtained by double-single emulation of the floating-point arithmetic
in accuracy-critical parts of the algorithm. For the slow dynamics of a
supercooled binary Lennard-Jones mixture, we demonstrate that the use of
single-floating point precision may result in quantitatively and even
physically wrong results. For simulations of a Lennard-Jones fluid, the
described implementation shows speedup factors of up to 80 compared to a serial
implementation for the CPU, and a single GPU was found to compare with a
parallelised MD simulation using 64 distributed cores.Comment: 12 pages, 7 figures, to appear in Comp. Phys. Comm., HALMD package
licensed under the GPL, see http://research.colberg.org/projects/halm
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