Unbiased Shape Compactness for Segmentation

We propose to constrain segmentation functionals with a dimensionless, unbiased and position-independent shape compactness prior, which we solve efficiently with an alternating direction method of multipliers (ADMM). Involving a squared sum of pairwise potentials, our prior results in a challenging high-order optimization problem, which involves dense (fully connected) graphs. We split the problem into a sequence of easier sub-problems, each performed efficiently at each iteration: (i) a sparse-matrix inversion based on Woodbury identity, (ii) a closed-form solution of a cubic equation and (iii) a graph-cut update of a sub-modular pairwise sub-problem with a sparse graph. We deploy our prior in an energy minimization, in conjunction with a supervised classifier term based on CNNs and standard regularization constraints. We demonstrate the usefulness of our energy in several medical applications. In particular, we report comprehensive evaluations of our fully automated algorithm over 40 subjects, showing a competitive performance for the challenging task of abdominal aorta segmentation in MRI.Comment: Accepted at MICCAI 201

Glutamate-mediated blood-brain barrier opening. implications for neuroprotection and drug delivery

The blood-brain barrier is a highly selective anatomical and functional interface allowing a unique environment for neuro-glia networks. Blood-brain barrier dysfunction is common in most brain disorders and is associated with disease course and delayed complications. However, the mechanisms underlying blood-brain barrier opening are poorly understood. Here we demonstrate the role of the neurotransmitter glutamate in modulating early barrier permeability in vivo Using intravital microscopy, we show that recurrent seizures and the associated excessive glutamate release lead to increased vascular permeability in the rat cerebral cortex, through activation of NMDA receptors. NMDA receptor antagonists reduce barrier permeability in the peri-ischemic brain, whereas neuronal activation using high-intensity magnetic stimulation increases barrier permeability and facilitates drug delivery. Finally, we conducted a double-blind clinical trial in patients with malignant glial tumors, using contrast-enhanced magnetic resonance imaging to quantitatively assess blood-brain barrier permeability. We demonstrate the safety of stimulation that efficiently increased blood-brain barrier permeability in 10 of 15 patients with malignant glial tumors. We suggest a novel mechanism for the bidirectional modulation of brain vascular permeability toward increased drug delivery and prevention of delayed complications in brain disorders. SIGNIFICANCE STATEMENT: In this study, we reveal a new mechanism that governs blood-brain barrier (BBB) function in the rat cerebral cortex, and, by using the discovered mechanism, we demonstrate bidirectional control over brain endothelial permeability. Obviously, the clinical potential of manipulating BBB permeability for neuroprotection and drug delivery is immense, as we show in preclinical and proof-of-concept clinical studies. This study addresses an unmet need to induce transient BBB opening for drug delivery in patients with malignant brain tumors and effectively facilitate BBB closure in neurological disorders

Autoresonance in a Dissipative System

We study the autoresonant solution of Duffing's equation in the presence of dissipation. This solution is proved to be an attracting set. We evaluate the maximal amplitude of the autoresonant solution and the time of transition from autoresonant growth of the amplitude to the mode of fast oscillations. Analytical results are illustrated by numerical simulations.Comment: 22 pages, 3 figure

Temperature dependence of plasmonic terahertz absorption in grating-gate gallium-nitride transistor structures

Strong plasmon resonances have been observed in the terahertz transmission spectra (1-5 THz) of large-area slit-grating-gate AlGaN/GaN-based high-electron-mobility transistor (HEMT) structures at temperatures from 10 to 170 K. The resonance frequencies correspond to the excitation of plasmons with wave vectors equal to the reciprocal lattice vectors of the metal grating, which serves both as a gate electrode for the HEMT and a coupler between plasmons and incident terahertz radiation. Wide tunability of the resonances by the applied gate voltage demonstrates potential of these devices for terahertz applications

Iterative graph cuts for image segmentation with a nonlinear statistical shape prior

Shape-based regularization has proven to be a useful method for delineating objects within noisy images where one has prior knowledge of the shape of the targeted object. When a collection of possible shapes is available, the specification of a shape prior using kernel density estimation is a natural technique. Unfortunately, energy functionals arising from kernel density estimation are of a form that makes them impossible to directly minimize using efficient optimization algorithms such as graph cuts. Our main contribution is to show how one may recast the energy functional into a form that is minimizable iteratively and efficiently using graph cuts.Comment: Revision submitted to JMIV (02/24/13

GRMA: Generalized Range Move Algorithms for the efficient optimization of MRFs

Markov Random Fields (MRF) have become an important tool for many vision applications, and the optimization of MRFs is a problem of fundamental importance. Recently, Veksler and Kumar et al. proposed the range move algorithms, which are some of the most successful optimizers. Instead of considering only two labels as in previous move-making algorithms, they explore a large search space over a range of labels in each iteration, and significantly outperform previous move-making algorithms. However, two problems have greatly limited the applicability of range move algorithms: 1) They are limited in the energy functions they can handle (i.e., only truncated convex functions); 2) They tend to be very slow compared to other move-making algorithms (e.g., �-expansion and ��-swap). In this paper, we propose two generalized range move algorithms (GRMA) for the efficient optimization of MRFs. To address the first problem, we extend the GRMAs to more general energy functions by restricting the chosen labels in each move so that the energy function is submodular on the chosen subset. Furthermore, we provide a feasible sufficient condition for choosing these subsets of labels. To address the second problem, we dynamically obtain the iterative moves by solving set cover problems. This greatly reduces the number of moves during the optimization.We also propose a fast graph construction method for the GRMAs. Experiments show that the GRMAs offer a great speedup over previous range move algorithms, while yielding competitive solutions

Nonlinear Lattice Waves in Random Potentials

Localization of waves by disorder is a fundamental physical problem encompassing a diverse spectrum of theoretical, experimental and numerical studies in the context of metal-insulator transition, quantum Hall effect, light propagation in photonic crystals, and dynamics of ultra-cold atoms in optical arrays. Large intensity light can induce nonlinear response, ultracold atomic gases can be tuned into an interacting regime, which leads again to nonlinear wave equations on a mean field level. The interplay between disorder and nonlinearity, their localizing and delocalizing effects is currently an intriguing and challenging issue in the field. We will discuss recent advances in the dynamics of nonlinear lattice waves in random potentials. In the absence of nonlinear terms in the wave equations, Anderson localization is leading to a halt of wave packet spreading. Nonlinearity couples localized eigenstates and, potentially, enables spreading and destruction of Anderson localization due to nonintegrability, chaos and decoherence. The spreading process is characterized by universal subdiffusive laws due to nonlinear diffusion. We review extensive computational studies for one- and two-dimensional systems with tunable nonlinearity power. We also briefly discuss extensions to other cases where the linear wave equation features localization: Aubry-Andre localization with quasiperiodic potentials, Wannier-Stark localization with dc fields, and dynamical localization in momentum space with kicked rotors.Comment: 45 pages, 19 figure