47,461 research outputs found
Learning Deep Similarity Metric for 3D MR-TRUS Registration
Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance
(MR) images for guiding targeted prostate biopsy has significantly improved the
biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image
registration. However, it is very challenging to obtain a robust automatic
MR-TRUS registration due to the large appearance difference between the two
imaging modalities. The work presented in this paper aims to tackle this
problem by addressing two challenges: (i) the definition of a suitable
similarity metric and (ii) the determination of a suitable optimization
strategy.
Methods: This work proposes the use of a deep convolutional neural network to
learn a similarity metric for MR-TRUS registration. We also use a composite
optimization strategy that explores the solution space in order to search for a
suitable initialization for the second-order optimization of the learned
metric. Further, a multi-pass approach is used in order to smooth the metric
for optimization.
Results: The learned similarity metric outperforms the classical mutual
information and also the state-of-the-art MIND feature based methods. The
results indicate that the overall registration framework has a large capture
range. The proposed deep similarity metric based approach obtained a mean TRE
of 3.86mm (with an initial TRE of 16mm) for this challenging problem.
Conclusion: A similarity metric that is learned using a deep neural network
can be used to assess the quality of any given image registration and can be
used in conjunction with the aforementioned optimization framework to perform
automatic registration that is robust to poor initialization.Comment: To appear on IJCAR
Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion
Learning policies for bipedal locomotion can be difficult, as experiments are
expensive and simulation does not usually transfer well to hardware. To counter
this, we need al- gorithms that are sample efficient and inherently safe.
Bayesian Optimization is a powerful sample-efficient tool for optimizing
non-convex black-box functions. However, its performance can degrade in higher
dimensions. We develop a distance metric for bipedal locomotion that enhances
the sample-efficiency of Bayesian Optimization and use it to train a 16
dimensional neuromuscular model for planar walking. This distance metric
reflects some basic gait features of healthy walking and helps us quickly
eliminate a majority of unstable controllers. With our approach we can learn
policies for walking in less than 100 trials for a range of challenging
settings. In simulation, we show results on two different costs and on various
terrains including rough ground and ramps, sloping upwards and downwards. We
also perturb our models with unknown inertial disturbances analogous with
differences between simulation and hardware. These results are promising, as
they indicate that this method can potentially be used to learn control
policies on hardware.Comment: To appear in International Conference on Humanoid Robots (Humanoids
'2016), IEEE-RAS. (Rika Antonova and Akshara Rai contributed equally
Deep Kernels for Optimizing Locomotion Controllers
Sample efficiency is important when optimizing parameters of locomotion
controllers, since hardware experiments are time consuming and expensive.
Bayesian Optimization, a sample-efficient optimization framework, has recently
been widely applied to address this problem, but further improvements in sample
efficiency are needed for practical applicability to real-world robots and
high-dimensional controllers. To address this, prior work has proposed using
domain expertise for constructing custom distance metrics for locomotion. In
this work we show how to learn such a distance metric automatically. We use a
neural network to learn an informed distance metric from data obtained in
high-fidelity simulations. We conduct experiments on two different controllers
and robot architectures. First, we demonstrate improvement in sample efficiency
when optimizing a 5-dimensional controller on the ATRIAS robot hardware. We
then conduct simulation experiments to optimize a 16-dimensional controller for
a 7-link robot model and obtain significant improvements even when optimizing
in perturbed environments. This demonstrates that our approach is able to
enhance sample efficiency for two different controllers, hence is a fitting
candidate for further experiments on hardware in the future.Comment: (Rika Antonova and Akshara Rai contributed equally
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