Dissipative or Conservative cosmology with dark energy ?

All evolutional paths for all admissible initial conditions of FRW cosmological models with dissipative dust fluid (described by dark matter, baryonic matter and dark energy) are analyzed using dynamical system approach. With that approach, one is able to see how generic the class of solutions leading to the desired property -- acceleration -- is. The theory of dynamical systems also offers a possibility of investigating all possible solutions and their stability with tools of Newtonian mechanics of a particle moving in a 1-dimensional potential which is parameterized by the cosmological scale factor. We demonstrate that flat cosmology with bulk viscosity can be treated as a conservative system with a potential function of the Chaplygin gas type. We also confront viscous models with SNIa observations. The best fitted models are obtained by minimizing the $\chi^{2}$ function which is illustrated by residuals and $\chi^{2}$ levels in the space of model independent parameters. The general conclusion is that SNIa data supports the viscous model without the cosmological constant. The obtained values of $\chi^{2}$ statistic are comparable for both the viscous model and LCDM model. The Bayesian information criteria are used to compare the models with different power law parameterization of viscous effects. Our result of this analysis shows that SNIa data supports viscous cosmology more than the LCDM model if the coefficient in viscosity parameterization is fixed. The Bayes factor is also used to obtain the posterior probability of the model.Comment: RevTeX4, 23 pages, 12 figures; new part on general properties of dissipative FRW models (v.2); (v5) published versio

Automatic Segmentation of the Lumbar Spine from Medical Images

Segmentation of the lumbar spine in 3D is a necessary step in numerous medical applications, but remains a challenging problem for computational methods due to the complex and varied shape of the anatomy and the noise and other artefacts often present in the images. While manual annotation of anatomical objects such as vertebrae is often carried out with the aid of specialised software, obtaining even a single example can be extremely time-consuming. Automating the segmentation process is the only feasible way to obtain accurate and reliable segmentations on any large scale. This thesis describes an approach for automatic segmentation of the lumbar spine from medical images; specifically those acquired using magnetic resonance imaging (MRI) and computed tomography (CT). The segmentation problem is formulated as one of assigning class labels to local clustered regions of an image (called superpixels in 2D or supervoxels in 3D). Features are introduced in 2D and 3D which can be used to train a classifier for estimating the class labels of the superpixels or supervoxels. Spatial context is introduced by incorporating the class estimates into a conditional random field along with a learned pairwise metric. Inference over the resulting model can be carried out very efficiently, enabling an accurate pixel- or voxel-level segmentation to be recovered from the labelled regions. In contrast to most previous work in the literature, the approach does not rely on explicit prior shape information. It therefore avoids many of the problems associated with these methods, such as the need to construct a representative prior model of anatomical shape from training data and the approximate nature of the optimisation. The general-purpose nature of the proposed method means that it can be used to accurately segment both vertebrae and intervertebral discs from medical images without fundamental change to the model. Evaluation of the approach shows it to obtain accurate and robust performance in the presence of significant anatomical variation. The median average symmetric surface distances for 2D vertebra segmentation were 0.27mm on MRI data and 0.02mm on CT data. For 3D vertebra segmentation the median surface distances were 0.90mm on MRI data and 0.20mm on CT data. For 3D intervertebral disc segmentation a median surface distance of 0.54mm was obtained on MRI data

What the study of spinal cord injured patients can tell us about the significance of the body in cognition

Although in the last three decades philosophers, psychologists and neuroscientists have produced numerous studies on human cognition, the debate concerning its nature is still heated and current views on the subject are somewhat antithetical. On the one hand, there are those who adhere to a view implying ‘disembodiment’ which suggests that cognition is based entirely on symbolic processes. On the other hand, a family of theories referred to as the Embodied Cognition Theories (ECT) postulate that creating and maintaining cognition is linked with varying degrees of inherence to somatosensory and motor representations. Spinal cord injury induces a massive body-brain disconnection with the loss of sensory and motor bodily functions below the lesion level but without directly affecting the brain. Thus, SCI may represent an optimal model for testing the role of the body in cognition. In this review, we describe post-lesional cognitive modifications in relation to body, space and action representations and various instances of ECT. We discuss the interaction between body-grounded and symbolic processes in adulthood with relevant modifications after body-brain disconnection

Machine Learning Approaches to Human Body Shape Analysis

Soft biometrics, biomedical sciences, and many other fields of study pay particular attention to the study of the geometric description of the human body, and its variations. Although multiple contributions, the interest is particularly high given the non-rigid nature of the human body, capable of assuming different poses, and numerous shapes due to variable body composition. Unfortunately, a well-known costly requirement in data-driven machine learning, and particularly in the human-based analysis, is the availability of data, in the form of geometric information (body measurements) with related vision information (natural images, 3D mesh, etc.). We introduce a computer graphics framework able to generate thousands of synthetic human body meshes, representing a population of individuals with stratified information: gender, Body Fat Percentage (BFP), anthropometric measurements, and pose. This contribution permits an extensive analysis of different bodies in different poses, avoiding the demanding, and expensive acquisition process. We design a virtual environment able to take advantage of the generated bodies, to infer the body surface area (BSA) from a single view. The framework permits to simulate the acquisition process of newly introduced RGB-D devices disentangling different noise components (sensor noise, optical distortion, body part occlusions). Common geometric descriptors in soft biometric, as well as in biomedical sciences, are based on body measurements. Unfortunately, as we prove, these descriptors are not pose invariant, constraining the usability in controlled scenarios. We introduce a differential geometry approach assuming body pose variations as isometric transformations of the body surface, and body composition changes covariant to the body surface area. This setting permits the use of the Laplace-Beltrami operator on the 2D body manifold, describing the body with a compact, efficient, and pose invariant representation. We design a neural network architecture able to infer important body semantics from spectral descriptors, closing the gap between abstract spectral features, and traditional measurement-based indices. Studying the manifold of body shapes, we propose an innovative generative adversarial model able to learn the body shapes. The method permits to generate new bodies with unseen geometries as a walk on the latent space, constituting a significant advantage over traditional generative methods

Functional and structural MRI image analysis for brain glial tumors treatment

Cotutela con il Dipartimento di Biotecnologie e Scienze della Vita, Universiità degli Studi dell'Insubria.openThis Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient.openInformaticaPedoia, ValentinaPedoia, Valentin

Functional and structural MRI image analysis for brain glial tumors treatment

This Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available