An active contour-based atlas registration model applied to automatic subthalamic nucleus targeting on MRI: method and validation.

This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert's variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.

Active Contour-Based Segmentation of Head and Neck with Adaptive Atlas Selection

This paper presents automated segmentation of structuresin the Head and Neck (H\&N) region, using an activecontour-based joint registration and segmentation model.A new atlas selection strategy is also used. Segmentationis performed based on the dense deformation fieldcomputed from the registration of selected structures inthe atlas image that have distinct boundaries, onto thepatient's image. This approach results in robustsegmentation of the structures of interest, even in thepresence of tumors, or anatomical differences between theatlas and the patient image. For each patient, an atlasimage is selected from the available atlas-database,based on the similarity metric value, computed afterperforming an affine registration between each image inthe atlas-database and the patient's image. Unlike manyof the previous approaches in the literature, thesimilarity metric is not computed over the entire imageregion; rather, it is computed only in the regions ofsoft tissue structures to be segmented. Qualitative andquantitative evaluation of the results is presented.

The inhibition of fat cell proliferation by n-3 fatty acids in dietary obese mice.

ABSTRACT: BACKGROUND: Long-chain n-3 polyunsaturated fatty acids (LC n-3 PUFA) of marine origin exert multiple beneficial effects on health. Our previous study in mice showed that reduction of adiposity by LC n-3 PUFA was associated with both, a shift in adipose tissue metabolism and a decrease in tissue cellularity. The aim of this study was to further characterize the effects of LC n-3 PUFA on fat cell proliferation and differentiation in obese mice. METHODS: A model of inducible and reversible lipoatrophy (aP2-Cre-ERT2 PPARgammaL2/L2 mice) was used, in which the death of mature adipocytes could be achieved by a selective ablation of peroxisome proliferator-activated receptor gamma in response to i.p. injection of tamoxifen. Before the injection, obesity was induced in male mice by 8-week-feeding a corn oil-based high-fat diet (cHF) and, subsequently, mice were randomly assigned (day 0) to one of the following groups: (i) mice injected by corn-oil-vehicle only, i.e."control" mice, and fed cHF; (ii) mice injected by tamoxifen in corn oil, i.e. "mutant" mice, fed cHF; (iii) control mice fed cHF diet with 15% of dietary lipids replaced by LC n-3 PUFA concentrate (cHF+F); and (iv) mutant mice fed cHF+F. Blood and tissue samples were collected at days 14 and 42. RESULTS: Mutant mice achieved a maximum weight loss within 10 days post-injection, followed by a compensatory body weight gain, which was significantly faster in the cHF as compared with the cHF+F mutant mice. Also in control mice, body weight gain was depressed in response to dietary LC n-3 PUFA. At day 42, body weights in all groups stabilized, with no significant differences in adipocyte size between the groups, although body weight and adiposity was lower in the cHF+F as compared with the cHF mice, with a stronger effect in the mutant than in control mice. Gene expression analysis documented depression of adipocyte maturation during the reconstitution of adipose tissue in the cHF+F mutant mice. CONCLUSION: Dietary LC n-3 PUFA could reduce both hypertrophy and hyperplasia of fat cells in vivo. Results are in agreement with the involvement of fat cell turnover in control of adiposity.

Stable carbon isotope Composition of c9,t11-conjugated linoleic acid in cow's milk as related to dietary fatty acids

This study explores the potential use of stable carbon isotope ratios (delta C-13) of single fatty acids (FA) as tracers for the transformation of FA from diet to milk, with focus on the metabolic origin of c9,t11-18:2. For this purpose, dairy cows were fed diets based exclusively on C-3 and C-4 plants. The FA in milk and feed were fractionated by silver-ion thin-layer chromatography and analyzed for their delta C-13 values. Mean delta C-13 values of FA from C-3 milk were lower compared to those from C-4 milk (-30.1aEuro degrees vs. -24.9aEuro degrees, respectively). In both groups the most negative delta C-13 values of all FA analyzed were measured for c9,t11-18:2 (C-3 milk = -37.0 +/- A 2.7aEuro degrees; C-4 milk -31.4 +/- A 1.4aEuro degrees). Compared to the dietary precursors 18:2n-6 and 18:3n-3, no significant C-13-depletion was measured in t11-18:1. This suggests that the delta C-13-change in c9,t11-18:2 did not originate from the microbial biohydrogenation in the rumen, but most probably from endogenous desaturation of t11-18:1. It appears that the natural delta C-13 differences in some dietary FA are at least partly preserved in milk FA. Therefore, carbon isotope analyses of individual FA could be useful for studying metabolic transformation processes in ruminants.

Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

In this paper, we present the segmentation of the headand neck lymph node regions using a new active contourbased atlas registration model. We propose to segment thelymph node regions without directly including them in theatlas registration process; instead, they are segmentedusing the dense deformation field computed from theregistration of the atlas structures with distinctboundaries. This approach results in robust and accuratesegmentation of the lymph node regions even in thepresence of significant anatomical variations between theatlas-image and the patient's image to be segmented. Wealso present a quantitative evaluation of lymph noderegions segmentation using various statistical as well asgeometrical metrics: sensitivity, specificity, dicesimilarity coefficient and Hausdorff distance. Acomparison of the proposed method with two other state ofthe art methods is presented. The robustness of theproposed method to the atlas selection, in segmenting thelymph node regions, is also evaluated.

Statistical assessment of dataset shift and model portability in multi-angle in-track image acquisitions

In this study we propose an evaluation of the angular effects altering the spectral response of the land-cover over multi-angle remote sensing image acquisitions. The shift in the statistical distribution of the pixels observed in an in-track sequence of WorldView-2 images is analyzed by means of a kernel-based measure of distance between probability distributions. Afterwards, the portability of supervised classifiers across the sequence is investigated by looking at the evolution of the classification accuracy with respect to the changing observation angle. In this context, the efficiency of various physically and statistically based preprocessing methods in obtaining angle-invariant data spaces is compared and possible synergies are discussed.

Compliance with the Swiss Society for Nutrition's dietary recommendations in the population of Geneva, Switzerland: a 10-year trend study (1999-2009).

The trends in compliance with the dietary recommendations of the Swiss Society for Nutrition in the Geneva population were assessed for the period from 1999 to 2009 using 10 cross-sectional, population-based surveys (Bus Santé study) with a total of 9,320 participants aged 35 to 75 years (50% women). Dietary intake was assessed using a self-administered, validated, semi-quantitative food frequency questionnaire. Trends were assessed by logistic regression adjusting for age, smoking status, education, and nationality using survey year as the independent variable. After excluding participants with extreme intakes, the percentage of participants with a cholesterol intake of <300 mg/day increased from 40.8% in 1999 to 43.6% in 2009 for men (multivariate-adjusted P for trend=0.04) and from 57.8% to 61.4% in women (multivariate-adjusted P for trend=0.06). Calcium intake >1 g/day decreased from 53.3% to 46% in men and from 47.6% to 40.7% in women (multivariate-adjusted P for trend<0.001). Adequate iron intake decreased from 68.3% to 65.3% in men and from 13.3% to 8.4% in women (multivariate-adjusted P for trend<0.001). Conversely, no significant changes were observed for carbohydrates, protein, total fat (including saturated, monounsaturated, and polyunsaturated fatty acids), fiber, and vitamins D and A. We conclude that the quality of the Swiss diet did not improve between 1999 and 2009 and that intakes deviate substantially from expert recommendations for health promotion and chronic disease risk reduction.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Exemplar-based interpolation of sparsely sampled images

A nonlocal variational formulation for interpolating a sparsel sampled image is introduced in this paper. The proposed variational formulation, originally motivated by image inpainting problems, encouragesthe transfer of information between similar image patches, following the paradigm of exemplar-based methods. Contrary to the classical inpaintingproblem, no complete patches are available from the sparse imagesamples, and the patch similarity criterion has to be redefined as here proposed. Initial experimental results with the proposed framework, at very low sampling densities, are very encouraging. We also explore somedepartures from the variational setting, showing a remarkable ability to recover textures at low sampling densities.

A review of atlas-based segmentation for magnetic resonance brain images.

Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented.

Effect of Video Camera-Based Remote Roadway Condition Monitoring on Snow Removal-Related Maintenance Operations. 2014

Remote monitoring through the use of cameras is widely utilized for traffic operation, but has not been utilized widely for roadway maintenance operations. The Utah Department of Transportation (UDOT) has implemented a new remote monitoring system, referred to as a Cloud-enabled Remote Video Streaming (CRVS) camera system for snow removal-related maintenance operations in the winter. The purpose of this study was to evaluate the effectiveness of the use of the CRVS camera system in snow removal-related maintenance operations. This study was conducted in two parts: opinion surveys of maintenance station supervisors and an analysis on snow removal-related maintenance costs. The responses to the opinion surveys mostly displayed positive reviews of the use of the CRVS cameras. On a scale of 1 (least effective) to 5 (most effective), the average overall effectiveness given by the station supervisors was 4.3. An expedition trip for this study was defined as a trip that was made to just check the roadways if snow-removal was necessary. The average of the responses received from surveys was calculated to be a 33 percent reduction in expedition trips. For the second part of this study, an analysis was performed on the snow removal-related maintenance cost data provided by UDOT to see if the installation of a CRVS camera had an effect in reducing expedition trips. This expedition cost comparison was performed for 10 sets of maintenance stations within Utah. It was difficult to make any definitive inferences from the comparison of expedition costs over the years for which precipitation and expedition cost data were available; hence a statistical analysis was performed using the Mixed Model ANOVA. This analysis resulted in an average of 14 percent higher ratio of expedition costs at maintenance stations with a CRVS camera before the installation of the camera compared to the ratio of expedition costs after the installation of the camera. This difference was not proven to be statistically significant at the 95 percent confident level, but indicated that the installation of CRVS cameras was on the average helpful in reducing expedition costs and may be considered practically significant. It is recommended that more detailed and consistent maintenance cost records be prepared for accurate analysis of cost records for this type of study in the future.

Paleohydrological Reconstruction from Late Holocene Records in Interdune Lakes (N'Guigmi, Northern Bank of the Lake Chad, Niger)

An old erg covers the northern part of the Lake Chad basin. This dune landform allowed the formation of many inter- dune ponds of various sizes. Still present in certain zones where the groundwater level is high (e.g. Kanem, southern Manga), these ponds formed in the past a vast network of lacustrine microsystems, as shown by the nature and the dis- tribution of their deposits. In the Manga, these interdune deposits represent the main sedimentary records of the Holo- cene environmental succession. Their paleobiological (pollens, diatoms, ostracods) and geochemical (δ18O, δ13C, Sr/ Ca) contents are often the basis for paleoenvironmental reconstruction. On the other hand, their sedimentological char- acters are rarely exploited. This study of palustro-lacustrine deposits of the Holocene N'Guigmi lake (northern bank of the Lake Chad; Niger) is based on the relationships between the sedimentological features and the climato-hydrological fluctuations. The mineralogical parameters (e.g. calcium carbonate content, clay mineralogy) and the nature of autoch- thonous mineralization (i.e. amorphous silica, clays, calcium carbonates) can be interpreted using a straightforward hy- dro-sedimentary model. Established to explain the geochemical dynamics of Lake Chad, this model is based on a bio- geochemical cycle of the main elements (i.e. silicium, calcium) directly controlled by the local hydrological balance (i.e. rainfall/evaporation ratio). All these results show that a detailed study of sedimentological features can provide impor- tant paleohydrological informations about the regional aridification since ca 6500 14C BP.

The Mixed Management of Patients' Medical Records: Responsibility Sharing Between the Patient and the Physician

Through this article, we propose a mixed management of patients' medical records, so as to share responsibilities between the patient and the Medical Practitioner by making Patients responsible for the validation of their administrative information, and MPs responsible for the validation of their Patients' medical information. Our proposal can be considered a solution to the main problem faced by patients, health practitioners and the authorities, namely the gathering and updating of administrative and medical data belonging to the patient in order to accurately reconstitute a patient's medical history. This method is based on two processes. The aim of the first process is to provide a patient's administrative data, in order to know where and when the patient received care (name of the health structure or health practitioner, type of care: out patient or inpatient). The aim of the second process is to provide a patient's medical information and to validate it under the accountability of the Medical Practitioner with the help of the patient if needed. During these two processes, the patient's privacy will be ensured through cryptographic hash functions like the Secure Hash Algorithm, which allows pseudonymisation of a patient's identity. The proposed Medical Record Search Engines will be able to retrieve and to provide upon a request formulated by the Medical ractitioner all the available information concerning a patient who has received care in different health structures without divulging the patient's identity. Our method can lead to improved efficiency of personal medical record management under the mixed responsibilities of the patient and the MP.

The Learning Affect Monitor (LAM) - A computer-based system integrating quantitative and qualitative assessment of affective states in daily life.

The Learning Affect Monitor (LAM) is a new computer-based assessment system integrating basic dimensional evaluation and discrete description of affective states in daily life, based on an autonomous adapting system. Subjects evaluate their affective states according to a tridimensional space (valence and activation circumplex as well as global intensity) and then qualify it using up to 30 adjective descriptors chosen from a list. The system gradually adapts to the user, enabling the affect descriptors it presents to be increasingly relevant. An initial study with 51 subjects, using a 1 week time-sampling with 8 to 10 randomized signals per day, produced n = 2,813 records with good reliability measures (e.g., response rate of 88.8%, mean split-half reliability of .86), user acceptance, and usability. Multilevel analyses show circadian and hebdomadal patterns, and significant individual and situational variance components of the basic dimension evaluations. Validity analyses indicate sound assignment of qualitative affect descriptors in the bidimensional semantic space according to the circumplex model of basic affect dimensions. The LAM assessment module can be implemented on different platforms (palm, desk, mobile phone) and provides very rapid and meaningful data collection, preserving complex and interindividually comparable information in the domain of emotion and well-being.

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.