Computer-aided segmentation and estimation of indices in brain CT scans

The importance of neuro-imaging as one of the biomarkers for diagnosis and prognosis of pathologies and traumatic cases is well established. Doctors routinely perform linear measurements on neuro-images to ascertain severity and extent of the pathology or trauma from significant anatomical changes. However, it is a tedious and time consuming process and manually assessing and reporting on large volume of data is fraught with errors and variation. In this paper we present a novel technique for segmentation of significant anatomical landmarks using artificial neural networks and estimation of various ratios and indices performed on brain CT scans. The proposed method is efficient and robust in detecting and measuring sizes of anatomical structures on non-contrast CT scans and has been evaluated on images from subjects with ages between 5 to 85 years. Results show that our method has average ICC of ≥0.97 and, hence, can be used in processing data for further use in research and clinical environment

DeepPose: Human Pose Estimation via Deep Neural Networks

We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regressors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formulation which capitalizes on recent advances in Deep Learning. We present a detailed empirical analysis with state-of-art or better performance on four academic benchmarks of diverse real-world images.Comment: IEEE Conference on Computer Vision and Pattern Recognition, 201

Exploring self-similarity of complex cellular networks: The edge-covering method with simulated annealing and log-periodic sampling

Song, Havlin and Makse (2005) have recently used a version of the box-counting method, called the node-covering method, to quantify the self-similar properties of 43 cellular networks: the minimal number $N_V$ of boxes of size $\ell$ needed to cover all the nodes of a cellular network was found to scale as the power law $N_V \sim (\ell+1)^{-D_V}$ with a fractal dimension $D_V=3.53\pm0.26$. We propose a new box-counting method based on edge-covering, which outperforms the node-covering approach when applied to strictly self-similar model networks, such as the Sierpinski network. The minimal number $N_E$ of boxes of size $\ell$ in the edge-covering method is obtained with the simulated annealing algorithm. We take into account the possible discrete scale symmetry of networks (artifactual and/or real), which is visualized in terms of log-periodic oscillations in the dependence of the logarithm of $N_E$ as a function of the logarithm of $\ell$. In this way, we are able to remove the bias of the estimator of the fractal dimension, existing for finite networks. With this new methodology, we find that $N_E$ scales with respect to $\ell$ as a power law $N_E \sim \ell^{-D_E}$ with $D_E=2.67\pm0.15$ for the 43 cellular networks previously analyzed by Song, Havlin and Makse (2005). Bootstrap tests suggest that the analyzed cellular networks may have a significant log-periodicity qualifying a discrete hierarchy with a scaling ratio close to 2. In sum, we propose that our method of edge-covering with simulated annealing and log-periodic sampling minimizes the significant bias in the determination of fractal dimensions in log-log regressions.Comment: 19 elsart pages including 9 eps figure

From homogeneous to fractal normal and tumorous microvascular networks in the brain

We studied normal and tumorous three-dimensional (3D) microvascular networks in primate and rat brain. Tissues were prepared following a new preparation technique intended for high-resolution synchrotron tomography of microvascular networks. The resulting 3D images with a spatial resolution of less than the minimum capillary diameter permit a complete description of the entire vascular network for volumes as large as tens of cubic millimeters. The structural properties of the vascular networks were investigated by several multiscale methods such as fractal and power- spectrum analysis. These investigations gave a new coherent picture of normal and pathological complex vascular structures. They showed that normal cortical vascular networks have scale- invariant fractal properties on a small scale from 1.4 lm up to 40 to 65 lm. Above this threshold, vascular networks can be considered as homogeneous. Tumor vascular networks show similar characteristics, but the validity range of the fractal regime extend to much larger spatial dimensions. These 3D results shed new light on previous two dimensional analyses giving for the first time a direct measurement of vascular modules associated with vessel-tissue surface exchange

Recurrence networks - A novel paradigm for nonlinear time series analysis

This paper presents a new approach for analysing structural properties of time series from complex systems. Starting from the concept of recurrences in phase space, the recurrence matrix of a time series is interpreted as the adjacency matrix of an associated complex network which links different points in time if the evolution of the considered states is very similar. A critical comparison of these recurrence networks with similar existing techniques is presented, revealing strong conceptual benefits of the new approach which can be considered as a unifying framework for transforming time series into complex networks that also includes other methods as special cases. It is demonstrated that there are fundamental relationships between the topological properties of recurrence networks and the statistical properties of the phase space density of the underlying dynamical system. Hence, the network description yields new quantitative characteristics of the dynamical complexity of a time series, which substantially complement existing measures of recurrence quantification analysis

The influence of the noradrenergic system on optimal control of neural plasticity

Decision making under uncertainty is challenging for any autonomous agent. The challenge increases when the environment’s stochastic properties change over time, i.e., when the environment is volatile. In order to efficiently adapt to volatile environments, agents must primarily rely on recent outcomes to quickly change their decision strategies; in other words, they need to increase their knowledge plasticity. On the contrary, in stable environments, knowledge stability must be preferred to preserve useful information against noise. Here we propose that in mammalian brain, the locus coeruleus (LC) is one of the nuclei involved in volatility estimation and in the subsequent control of neural plasticity. During a reinforcement learning task, LC activation, measured by means of pupil diameter, coded both for environmental volatility and learning rate. We hypothesize that LC could be responsible, through norepinephrinic modulation, for adaptations to optimize decision making in volatile environments. We also suggest a computational model on the interaction between the anterior cingulate cortex (ACC) and LC for volatility estimation