Dense Deformation Field Estimation for Atlas Registration using the Active Contour Framework

In this paper, we propose a new paradigm to carry outthe registration task with a dense deformation fieldderived from the optical flow model and the activecontour method. The proposed framework merges differenttasks such as segmentation, regularization, incorporationof prior knowledge and registration into a singleframework. The active contour model is at the core of ourframework even if it is used in a different way than thestandard approaches. Indeed, active contours are awell-known technique for image segmentation. Thistechnique consists in finding the curve which minimizesan energy functional designed to be minimal when thecurve has reached the object contours. That way, we getaccurate and smooth segmentation results. So far, theactive contour model has been used to segment objectslying in images from boundary-based, region-based orshape-based information. Our registration technique willprofit of all these families of active contours todetermine a dense deformation field defined on the wholeimage. A well-suited application of our model is theatlas registration in medical imaging which consists inautomatically delineating anatomical structures. Wepresent results on 2D synthetic images to show theperformances of our non rigid deformation field based ona natural registration term. We also present registrationresults on real 3D medical data with a large spaceoccupying tumor substantially deforming surroundingstructures, which constitutes a high challenging problem.

Simultaneous cell morphometry and refractive index measurement with dual-wavelength digital holographic microscopy and dye-enhanced dispersion of perfusion medium.

Digital holographic microscopy (DHM) allows optical-path-difference (OPD) measurements with nanometric accuracy. OPD induced by transparent cells depends on both the refractive index (RI) of cells and their morphology. This Letter presents a dual-wavelength DHM that allows us to separately measure both the RI and the cellular thickness by exploiting an enhanced dispersion of the perfusion medium achieved by the utilization of an extracellular dye. The two wavelengths are chosen in the vicinity of the absorption peak of the dye, where the absorption is accompanied by a significant variation of the RI as a function of the wavelength.

Learning spatial filters for multispectral image segmentation

We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.

Auditory-visual multisensory interactions in humans: timing, topography, directionality, and sources.

Current models of brain organization include multisensory interactions at early processing stages and within low-level, including primary, cortices. Embracing this model with regard to auditory-visual (AV) interactions in humans remains problematic. Controversy surrounds the application of an additive model to the analysis of event-related potentials (ERPs), and conventional ERP analysis methods have yielded discordant latencies of effects and permitted limited neurophysiologic interpretability. While hemodynamic imaging and transcranial magnetic stimulation studies provide general support for the above model, the precise timing, superadditive/subadditive directionality, topographic stability, and sources remain unresolved. We recorded ERPs in humans to attended, but task-irrelevant stimuli that did not require an overt motor response, thereby circumventing paradigmatic caveats. We applied novel ERP signal analysis methods to provide details concerning the likely bases of AV interactions. First, nonlinear interactions occur at 60-95 ms after stimulus and are the consequence of topographic, rather than pure strength, modulations in the ERP. AV stimuli engage distinct configurations of intracranial generators, rather than simply modulating the amplitude of unisensory responses. Second, source estimations (and statistical analyses thereof) identified primary visual, primary auditory, and posterior superior temporal regions as mediating these effects. Finally, scalar values of current densities in all of these regions exhibited functionally coupled, subadditive nonlinear effects, a pattern increasingly consistent with the mounting evidence in nonhuman primates. In these ways, we demonstrate how neurophysiologic bases of multisensory interactions can be noninvasively identified in humans, allowing for a synthesis across imaging methods on the one hand and species on the other.

Management of patient dose and image noise in routine pediatric CT abdominal examinations.

The aim was to propose a strategy for finding reasonable compromises between image noise and dose as a function of patient weight. Weighted CT dose index (CTDI(w)) was measured on a multidetector-row CT unit using CTDI test objects of 16, 24 and 32 cm in diameter at 80, 100, 120 and 140 kV. These test objects were then scanned in helical mode using a wide range of tube currents and voltages with a reconstructed slice thickness of 5 mm. For each set of acquisition parameter image noise was measured and the Rose model observer was used to test two strategies for proposing a reasonable compromise between dose and low-contrast detection performance: (1) the use of a unique noise level for all test object diameters, and (2) the use of a unique dose efficacy level defined as the noise reduction per unit dose. Published data were used to define four weight classes and an acquisition protocol was proposed for each class. The protocols have been applied in clinical routine for more than one year. CTDI(vol) values of 6.7, 9.4, 15.9 and 24.5 mGy were proposed for the following weight classes: 2.5-5, 5-15, 15-30 and 30-50 kg with image noise levels in the range of 10-15 HU. The proposed method allows patient dose and image noise to be controlled in such a way that dose reduction does not impair the detection of low-contrast lesions. The proposed values correspond to high- quality images and can be reduced if only high-contrast organs are assessed.

Adaptive frequency tracking of the baseline ECG identifies the site of atrial fibrillation termination by catheter ablation

Multiple organization indices have been used to predict the outcome of stepwise catheter ablation in long-standing persistent atrial fibrillation (AF), however with limited success. Our study aims at developinginnovative organization indices from baseline ECG (i.e. during the procedure, before ablation) in orderto identify the site of AF termination by catheter ablation. Seventeen consecutive male patients (age60 ± 5 years, AF duration 7 ± 5 years) underwent a stepwise catheter ablation. Chest lead V6 was placedin the back (V6b). QRST cancelation was performed from chest leads V1 to V6b. Using an innovativeadaptive harmonic frequency tracking, two measures of AF organization were computed to quantify theharmonics components of ECG activity: (1) the adaptive phase difference variance (APD) between theAF harmonic components as a measure of AF regularity, and (2) and adaptive organization index (AOI)evaluating the cyclicity of the AF oscillations. Both adaptive indices were compared to indices computedusing a time-invariant approach: (1) ECG AF cycle length (AFCL), (2) the spectrum based organizationindex (OI), and (3) the time-invariant phase difference TIPD. Long-standing persistent AF was terminatedinto sinus rhythm or atrial tachycardia in 13/17 patients during stepwise ablation, 11 during left atriumablation (left terminated patients - LT), 2 during the right atrium ablation (right terminated patients -RT), and 4 were non terminated (NT) and required electrical cardioversion. Our findings showed that LTpatients were best separated from RT/NT before ablation by the duration of sustained AF and by AOI onchest lead V1 and APD from the dorsal lead V6b as compared to ECG AFCL, OI and TIPD, respectively. Ourresults suggest that adaptive measures of AF organization computed before ablation perform better thantime-invariant based indices for identifying patients whose AF will terminate during ablation within theleft atrium. These findings are indicative of a higher baseline organization in these patients that could beused to select candidates for the termination of AF by stepwise catheter ablation.© 2013 Elsevier Ltd. All rights reserved.

Integrated methodologies to study human transcriptional regulation from functional genomics data

Abstract : The human body is composed of a huge number of cells acting together in a concerted manner. The current understanding is that proteins perform most of the necessary activities in keeping a cell alive. The DNA, on the other hand, stores the information on how to produce the different proteins in the genome. Regulating gene transcription is the first important step that can thus affect the life of a cell, modify its functions and its responses to the environment. Regulation is a complex operation that involves specialized proteins, the transcription factors. Transcription factors (TFs) can bind to DNA and activate the processes leading to the expression of genes into new proteins. Errors in this process may lead to diseases. In particular, some transcription factors have been associated with a lethal pathological state, commonly known as cancer, associated with uncontrolled cellular proliferation, invasiveness of healthy tissues and abnormal responses to stimuli. Understanding cancer-related regulatory programs is a difficult task, often involving several TFs interacting together and influencing each other's activity. This Thesis presents new computational methodologies to study gene regulation. In addition we present applications of our methods to the understanding of cancer-related regulatory programs. The understanding of transcriptional regulation is a major challenge. We address this difficult question combining computational approaches with large collections of heterogeneous experimental data. In detail, we design signal processing tools to recover transcription factors binding sites on the DNA from genome-wide surveys like chromatin immunoprecipitation assays on tiling arrays (ChIP-chip). We then use the localization about the binding of TFs to explain expression levels of regulated genes. In this way we identify a regulatory synergy between two TFs, the oncogene C-MYC and SP1. C-MYC and SP1 bind preferentially at promoters and when SP1 binds next to C-NIYC on the DNA, the nearby gene is strongly expressed. The association between the two TFs at promoters is reflected by the binding sites conservation across mammals, by the permissive underlying chromatin states 'it represents an important control mechanism involved in cellular proliferation, thereby involved in cancer. Secondly, we identify the characteristics of TF estrogen receptor alpha (hERa) target genes and we study the influence of hERa in regulating transcription. hERa, upon hormone estrogen signaling, binds to DNA to regulate transcription of its targets in concert with its co-factors. To overcome the scarce experimental data about the binding sites of other TFs that may interact with hERa, we conduct in silico analysis of the sequences underlying the ChIP sites using the collection of position weight matrices (PWMs) of hERa partners, TFs FOXA1 and SP1. We combine ChIP-chip and ChIP-paired-end-diTags (ChIP-pet) data about hERa binding on DNA with the sequence information to explain gene expression levels in a large collection of cancer tissue samples and also on studies about the response of cells to estrogen. We confirm that hERa binding sites are distributed anywhere on the genome. However, we distinguish between binding sites near promoters and binding sites along the transcripts. The first group shows weak binding of hERa and high occurrence of SP1 motifs, in particular near estrogen responsive genes. The second group shows strong binding of hERa and significant correlation between the number of binding sites along a gene and the strength of gene induction in presence of estrogen. Some binding sites of the second group also show presence of FOXA1, but the role of this TF still needs to be investigated. Different mechanisms have been proposed to explain hERa-mediated induction of gene expression. Our work supports the model of hERa activating gene expression from distal binding sites by interacting with promoter bound TFs, like SP1. hERa has been associated with survival rates of breast cancer patients, though explanatory models are still incomplete: this result is important to better understand how hERa can control gene expression. Thirdly, we address the difficult question of regulatory network inference. We tackle this problem analyzing time-series of biological measurements such as quantification of mRNA levels or protein concentrations. Our approach uses the well-established penalized linear regression models where we impose sparseness on the connectivity of the regulatory network. We extend this method enforcing the coherence of the regulatory dependencies: a TF must coherently behave as an activator, or a repressor on all its targets. This requirement is implemented as constraints on the signs of the regressed coefficients in the penalized linear regression model. Our approach is better at reconstructing meaningful biological networks than previous methods based on penalized regression. The method is tested on the DREAM2 challenge of reconstructing a five-genes/TFs regulatory network obtaining the best performance in the "undirected signed excitatory" category. Thus, these bioinformatics methods, which are reliable, interpretable and fast enough to cover large biological dataset, have enabled us to better understand gene regulation in humans.

Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy.

We have developed a digital holographic microscope (DHM), in a transmission mode, especially dedicated to the quantitative visualization of phase objects such as living cells. The method is based on an original numerical algorithm presented in detail elsewhere [Cuche et al., Appl. Opt. 38, 6994 (1999)]. DHM images of living cells in culture are shown for what is to our knowledge the first time. They represent the distribution of the optical path length over the cell, which has been measured with subwavelength accuracy. These DHM images are compared with those obtained by use of the widely used phase contrast and Nomarski differential interference contrast techniques.

Effect of n-3 fatty acids on cerebral markers and the inflammatory response in sepsis

Introduction ICM+ software encapsulates our 20 years' experience in brain monitoring. It collects data from a variety of bedside monitors and produces time trends of parameters defi ned using confi gurable mathematical formulae. To date it is being used in nearly 40 clinical research centres worldwide. We present its application for continuous monitoring of cerebral autoregulation using near-infrared spectroscopy (NIRS). Methods Data from multiple bedside monitors are processed by ICM+ in real time using a large selection of signal processing methods. These include various time and frequency domain analysis functions as well as fully customisable digital fi lters. The fi nal results are displayed in a variety of ways including simple time trends, as well as time window based histograms, cross histograms, correlations, and so forth. All this allows complex information from bedside monitors to be summarized in a concise fashion and presented to medical and nursing staff in a simple way that alerts them to the development of various pathological processes. Results One hundred and fi fty patients monitored continuously with NIRS, arterial blood pressure (ABP) and intracranial pressure (ICP), where available, were included in this study. There were 40 severely headinjured adult patients, 27 SAH patients (NCCU, Cambridge); 60 patients undergoing cardiopulmonary bypass (John Hopkins Hospital, Baltimore) and 23 patients with sepsis (University Hospital, Basel). In addition, MCA fl ow velocity (FV) was monitored intermittently using transcranial Doppler. FV-derived and ICP-derived pressure reactivity indices (PRx, Mx), as well as NIRS-derived reactivity indices (Cox, Tox, Thx) were calculated and showed signifi cant correlation with each other in all cohorts. Errorbar charts showing reactivity index PRx versus CPP (optimal CPP chart) as well as similar curves for NIRS indices versus CPP and ABP were also demonstrated. Conclusions ICM+ software is proving to be a very useful tool for enhancing the battery of available means for monitoring cerebral vasoreactivity and potentially facilitating autoregulation guided therapy. Complexity of data analysis is also hidden inside loadable profi les, thus allowing investigators to take full advantage of validated protocols including advanced processing formulas.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.

Coronary magnetic resonance angiography for assessment of the stent lumen: a phantom study.

PURPOSE: To evaluate the feasibility of visualizing the stent lumen using coronary magnetic resonance angiography in vitro. MATERIAL AND METHODS: Nineteen different coronary stents were implanted in plastic tubes with an inner diameter of 3 mm. The tubes were positioned in a plastic container filled with gel and included in a closed flow circuit (constant flow 18 cm/sec). The magnetic resonance images were obtained with a dual inversion fast spin-echo sequence. For intraluminal stent imaging, subtraction images were calculated from scans with and without flow. Subsequently, intraluminal signal properties were objectively assessed and compared. RESULTS: As a function of the stent type, various degrees of in-stent signal attenuation were observed. Tantalum stents demonstrated minimal intraluminal signal attenuation. For nitinol stents, the stent lumen could be identified, but the intraluminal signal was markedly reduced. Steel stents resulted in the most pronounced intraluminal signal voids. CONCLUSIONS: With the present technique, radiofrequency penetration into the stents is strongly influenced by the stent material. Thesefindings may have important implicationsforfuture stent design and stent imaging strategies.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Comparative study of human erythrocytes by digital holographic microscopy, confocal microscopy, and impedance volume analyzer.

Red blood cell (RBC) parameters such as morphology, volume, refractive index, and hemoglobin content are of great importance for diagnostic purposes. Existing approaches require complicated calibration procedures and robust cell perturbation. As a result, reference values for normal RBC differ depending on the method used. We present a way for measuring parameters of intact individual RBCs by using digital holographic microscopy (DHM), a new interferometric and label-free technique with nanometric axial sensitivity. The results are compared with values achieved by conventional techniques for RBC of the same donor and previously published figures. A DHM equipped with a laser diode (lambda = 663 nm) was used to record holograms in an off-axis geometry. Measurements of both RBC refractive indices and volumes were achieved via monitoring the quantitative phase map of RBC by means of a sequential perfusion of two isotonic solutions with different refractive indices obtained by the use of Nycodenz (decoupling procedure). Volume of RBCs labeled by membrane dye Dil was analyzed by confocal microscopy. The mean cell volume (MCV), red blood cell distribution width (RDW), and mean cell hemoglobin concentration (MCHC) were also measured with an impedance volume analyzer. DHM yielded RBC refractive index n = 1.418 +/- 0.012, volume 83 +/- 14 fl, MCH = 29.9 pg, and MCHC 362 +/- 40 g/l. Erythrocyte MCV, MCH, and MCHC achieved by an impedance volume analyzer were 82 fl, 28.6 pg, and 349 g/l, respectively. Confocal microscopy yielded 91 +/- 17 fl for RBC volume. In conclusion, DHM in combination with a decoupling procedure allows measuring noninvasively volume, refractive index, and hemoglobin content of single-living RBCs with a high accuracy.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.