22,950 research outputs found
Adversarial Deformation Regularization for Training Image Registration Neural Networks
We describe an adversarial learning approach to constrain convolutional
neural network training for image registration, replacing heuristic smoothness
measures of displacement fields often used in these tasks. Using
minimally-invasive prostate cancer intervention as an example application, we
demonstrate the feasibility of utilizing biomechanical simulations to
regularize a weakly-supervised anatomical-label-driven registration network for
aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural
transrectal ultrasound (TRUS) images. A discriminator network is optimized to
distinguish the registration-predicted displacement fields from the motion data
simulated by finite element analysis. During training, the registration network
simultaneously aims to maximize similarity between anatomical labels that
drives image alignment and to minimize an adversarial generator loss that
measures divergence between the predicted- and simulated deformation. The
end-to-end trained network enables efficient and fully-automated registration
that only requires an MR and TRUS image pair as input, without anatomical
labels or simulated data during inference. 108 pairs of labelled MR and TRUS
images from 76 prostate cancer patients and 71,500 nonlinear finite-element
simulations from 143 different patients were used for this study. We show that,
with only gland segmentation as training labels, the proposed method can help
predict physically plausible deformation without any other smoothness penalty.
Based on cross-validation experiments using 834 pairs of independent validation
landmarks, the proposed adversarial-regularized registration achieved a target
registration error of 6.3 mm that is significantly lower than those from
several other regularization methods.Comment: Accepted to MICCAI 201
On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions
Matching dynamical systems, through different forms of conjugacies and
equivalences, has long been a fundamental concept, and a powerful tool, in the
study and classification of nonlinear dynamic behavior (e.g. through normal
forms). In this paper we will argue that the use of the Koopman operator and
its spectrum is particularly well suited for this endeavor, both in theory, but
also especially in view of recent data-driven algorithm developments. We
believe, and document through illustrative examples, that this can nontrivially
extend the use and applicability of the Koopman spectral theoretical and
computational machinery beyond modeling and prediction, towards what can be
considered as a systematic discovery of "Cole-Hopf-type" transformations for
dynamics.Comment: 34 pages, 10 figure
Turbulence model reduction by deep learning
A central problem of turbulence theory is to produce a predictive model for
turbulent fluxes. These have profound implications for virtually all aspects of
the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence
produces anomalous fluxes via cross-correlations between fluctuations. In this
work, we introduce a new, data-driven method for parameterizing these fluxes.
The method uses deep supervised learning to infer a reduced mean-field model
from a set of numerical simulations. We apply the method to a simple drift-wave
turbulence system and find a significant new effect which couples the particle
flux to the local \emph{gradient} of vorticity. Notably, here, this effect is
much stronger than the oft-invoked shear suppression effect. We also recover
the result via a simple calculation. The vorticity gradient effect tends to
modulate the density profile. In addition, our method recovers a model for
spontaneous zonal flow generation by negative viscosity, stabilized by
nonlinear and hyperviscous terms. We highlight the important role of symmetry
to implementation of the new method.Comment: To be published in Phys. Rev. E Rap. Comm. 6 pages, 7 figure
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