Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions

Significance: Two-dimensional (2-D) fully convolutional neural networks have been shown capable of producing maps of sO2 from 2-D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as they are limited by the 2-D nature of the training data when the problem is inherently three-dimensional (3-D), and they have not been tested with realistic images. Aim: To demonstrate the capability of deep neural networks to process whole 3-D images and output 3-D maps of vascular sO2 from realistic tissue models/images. Approach: Two separate fully convolutional neural networks were trained to produce 3-D maps of vascular blood oxygen saturation and vessel positions from multiwavelength simulated images of tissue models. Results: The mean of the absolute difference between the true mean vessel sO2 and the network output for 40 examples was 4.4% and the standard deviation was 4.5%. Conclusions: 3-D fully convolutional networks were shown capable of producing accurate sO2 maps using the full extent of spatial information contained within 3-D images generated under conditions mimicking real imaging scenarios. We demonstrate that networks can cope with some of the confounding effects present in real images such as limited-view artifacts and have the potential to produce accurate estimates in vivo

Modeling of a Segmented Electrode for Desynchronizing Deep Brain Stimulation

Deep brain stimulation (DBS) is an effective therapy for medically refractory movement disorders like Parkinson’s disease. The electrodes, implanted in the target area within the human brain, generate an electric field which activates nerve fibers and cell bodies in the vicinity. Even though the different target nuclei display considerable differences in their anatomical structure, only few types of electrodes are currently commercially available. It is desirable to adjust the electric field and in particular the volume of tissue activated around the electrode with respect to the corresponding target nucleus in a such way that side effects can be reduced. Furthermore, a more selective and partial activation of the target structure is desirable for an optimal application of novel stimulation strategies, e.g., coordinated reset neuromodulation. Hence we designed a DBS electrode with a segmented design allowing a more selective activation of the target structure. We created a finite element model (FEM) of the electrode and analyzed the volume of tissue activated for this electrode design. The segmented electrode activated an area in a targeted manner, of which the dimension and position relative to the electrode could be controlled by adjusting the stimulation parameters for each electrode contact. According to our computational analysis, this directed stimulation might be superior with respect to the occurrence of side effects and it enables the application of coordinated reset neuromodulation under optimal conditions

Exploring semantic inter-class relationships (SIR) for zero-shot action recognition

© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Automatically recognizing a large number of action categories from videos is of significant importance for video understanding. Most existing works focused on the design of more discriminative feature representation, and have achieved promising results when the positive samples are enough. However, very limited efforts were spent on recognizing a novel action without any positive exemplars, which is often the case in the real settings due to the large amount of action classes and the users' queries dramatic variations. To address this issue, we propose to perform action recognition when no positive exemplars of that class are provided, which is often known as the zero-shot learning. Different from other zero-shot learning approaches, which exploit attributes as the intermediate layer for the knowledge transfer, our main contribution is SIR, which directly leverages the semantic inter-class relationships between the known and unknown actions followed by label transfer learning. The inter-class semantic relationships are automatically measured by continuous word vectors, which learned by the skip-gram model using the large-scale text corpus. Extensive experiments on the UCF101 dataset validate the superiority of our method over fully-supervised approaches using few positive exemplars

Dynamic concept composition for zero-example event detection

© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars. In principle, zero-shot learning makes it possible to train an event detection model based on the assumption that events (e.g. birthday party) can be described by multiple mid-level semantic concepts (e.g. "blowing candle", "birthday cake"). Towards this goal, we first pre-Train a bundle of concept classifiers using data from other sources. Then we evaluate the semantic correlation of each concept w.r.t. the event of interest and pick up the relevant concept classifiers, which are applied on all test videos to get multiple prediction score vectors. While most existing systems combine the predictions of the concept classifiers with fixed weights, we propose to learn the optimal weights of the concept classifiers for each testing video by exploring a set of online available videos with freeform text descriptions of their content. To validate the effectiveness of the proposed approach, we have conducted extensive experiments on the latest TRECVID MEDTest 2014, MEDTest 2013 and CCV dataset. The experimental results confirm the superiority of the proposed approach

On Learned Operator Correction in Inverse Problems

We discuss the possibility of learning a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to obtain regularized reconstructions. This paper discusses the conceptual difficulty of learning such a forward model correction and proceeds to present a possible solution as a forward-adjoint correction that explicitly corrects in both data and solution spaces. We then derive conditions under which solutions to the variational problem with a learned correction converge to solutions obtained with the correct operator. The proposed approach is evaluated on an application to limited view photoacoustic tomography and compared to the established framework of the Bayesian approximation error method

Resonance modes in the standard piezoceramic shear geometry: A discussion based on finite element analysis

El pdf del artículo es el manuscrito de autor.Several authors developed methods for the complex characterization of piezoceramics from complex impedance measurements at resonance. Alemany et al. developed an automatic iterative method, applied and reported in a first publication to four modes of resonance: (1) the length extensional mode of a thickness poled rectangular bar; (2) the length extensional mode of long rods or rectangular bars, length poled; (3) the thickness extensional mode of a thin plate and (4) the thickness shear mode of a thin plate. In a second publication it was reported the application of the method to (5) the radial mode of a thin disk, thickness poled, the most mathematically complex geometry. The (2), (3), (4) and (5) modes of resonance are sufficient for the purpose of the determination of the full set of complex elastic, dielectric and piezoelectric coefficients of piezoceramics, a 6mm symmetry material. This work presents the results of the FEA modeling of a thin plate based on the characterization of a commercial ceramic. The comparison of the experimental resonance spectra and the FEA results obtained for elastically, dielectrically and piezoelectrically homogeneous samples is presented and discussed. The complex characterization for the first time of the shear mode of a new lead-free piezoceramic is also shown.This work was carried out under the projects PIRAMID (G5RD-CT-2001-00456) of the GROWTH Program of the EC and MAT 2001-4819-E, MAT2002-00463 and the Ramon y Cajal Program, of the Spanish CICyT, and has benefited from the synergy provided by the POlar ELEtroCERamics, POLECER, (G5RT-CT2001-05024) Thematic Network of the EC.Peer reviewe

Multistability in the Kuramoto model with synaptic plasticity

We present a simplified phase model for neuronal dynamics with spike timing-dependent plasticity (STDP). For asymmetric, experimentally observed STDP we find multistability: a coexistence of a fully synchronized, a fully desynchronized, and a variety of cluster states in a wide enough range of the parameter space. We show that multistability can occur only for asymmetric STDP, and we study how the coexistence of synchronization and desynchronization and clustering depends on the distribution of the eigenfrequencies. We test the efficacy of the proposed method on the Kuramoto model which is, de facto, one of the sample models for a description of the phase dynamics in neuronal ensembles

Convolutional Neural Network for Material Decomposition in Spectral CT Scans

Spectral computed tomography acquires energy-resolved data that allows recovery of densities of constituents of an object. This can be achieved by decomposing the measured spectral projection into material projections, and passing these decomposed projections through a tomographic reconstruction algorithm, to get the volumetric mass density of each material. Material decomposition is a nonlinear inverse problem that has been traditionally solved using model-based material decomposition algorithms. However, the forward model is difficult to estimate in real prototypes. Moreover, the traditional regularizers used to stabilized inversions are not fully relevant in the projection domain.In this study, we propose a deep-learning method for material decomposition in the projection domain. We validate our methodology with numerical phantoms of human knees that are created from synchrotron CT scans. We consider four different scans for training, and one for validation. The measurements are corrupted by Poisson noise, assuming that at most 10 5 photons hit the detector. Compared to a regularized Gauss-Newton algorithm, the proposed deep-learning approach provides a compromise between noise and resolution, which reduces the computation time by a factor of 100

Magnetic Resonance in the Spin-Peierls compound α′−NaV2O5\alpha'-NaV_2O_5

We present results from magnetic resonance measurements for 75-350 GHz in $\alpha$'-NaV$_{2}$O$_{5}$. The temperature dependence of the integrated intensity indicates that we observe transitions in the excited state. A quantitative description gives resonances in the triplet state at high symmetry points of the excitation spectrum of this Spin-Peierls compound. This energy has the same temperature dependence as the Spin-Peierls gap. Similarities and differences with the other inorganic compound CuGeO$_{3}$ are discussed.Comment: 2 pages, REVTEX, 3 figures. to be published in Phys.Rev.

Winner-take-all selection in a neural system with delayed feedback

We consider the effects of temporal delay in a neural feedback system with excitation and inhibition. The topology of our model system reflects the anatomy of the avian isthmic circuitry, a feedback structure found in all classes of vertebrates. We show that the system is capable of performing a `winner-take-all' selection rule for certain combinations of excitatory and inhibitory feedback. In particular, we show that when the time delays are sufficiently large a system with local inhibition and global excitation can function as a `winner-take-all' network and exhibit oscillatory dynamics. We demonstrate how the origin of the oscillations can be attributed to the finite delays through a linear stability analysis.Comment: 8 pages, 6 figure