Harmonic active contours.

We propose a segmentation method based on the geometric representation of images as 2-D manifolds embedded in a higher dimensional space. The segmentation is formulated as a minimization problem, where the contours are described by a level set function and the objective functional corresponds to the surface of the image manifold. In this geometric framework, both data-fidelity and regularity terms of the segmentation are represented by a single functional that intrinsically aligns the gradients of the level set function with the gradients of the image and results in a segmentation criterion that exploits the directional information of image gradients to overcome image inhomogeneities and fragmented contours. The proposed formulation combines this robust alignment of gradients with attractive properties of previous methods developed in the same geometric framework: 1) the natural coupling of image channels proposed for anisotropic diffusion and 2) the ability of subjective surfaces to detect weak edges and close fragmented boundaries. The potential of such a geometric approach lies in the general definition of Riemannian manifolds, which naturally generalizes existing segmentation methods (the geodesic active contours, the active contours without edges, and the robust edge integrator) to higher dimensional spaces, non-flat images, and feature spaces. Our experiments show that the proposed technique improves the segmentation of multi-channel images, images subject to inhomogeneities, and images characterized by geometric structures like ridges or valleys.

Model-based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Computed Tomography (CT) represents the standard imaging modality for tumor volume delineation for radiotherapy treatment planning of retinoblastoma despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a highly accurate definition of the tumor volume thanks to its high spatial resolution but it is not currently integrated in the treatment planning but used only for diagnosis and follow-up. Our ultimate goal is an automatic segmentation of gross tumor volume (GTV) in the 3D US, the segmentation of the organs at risk (OAR) in the CT and the registration of both modalities. In this paper, we present some preliminary results in this direction. We present 3D active contour-based segmentation of the eye ball and the lens in CT images; the presented approach incorporates the prior knowledge of the anatomy by using a 3D geometrical eye model. The automated segmentation results are validated by comparing with manual segmentations. Then, we present two approaches for the fusion of 3D CT and US images: (i) landmark-based transformation, and (ii) object-based transformation that makes use of eye ball contour information on CT and US images.

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.

Model-based Segmentation and Image Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

For radiotherapy treatment planning of retinoblastoma inchildhood, Computed Tomography (CT) represents thestandard method for tumor volume delineation, despitesome inherent limitations. CT scan is very useful inproviding information on physical density for dosecalculation and morphological volumetric information butpresents a low sensitivity in assessing the tumorviability. On the other hand, 3D ultrasound (US) allows ahigh accurate definition of the tumor volume thanks toits high spatial resolution but it is not currentlyintegrated in the treatment planning but used only fordiagnosis and follow-up. Our ultimate goal is anautomatic segmentation of gross tumor volume (GTV) in the3D US, the segmentation of the organs at risk (OAR) inthe CT and the registration of both. In this paper, wepresent some preliminary results in this direction. Wepresent 3D active contour-based segmentation of the eyeball and the lens in CT images; the presented approachincorporates the prior knowledge of the anatomy by usinga 3D geometrical eye model. The automated segmentationresults are validated by comparing with manualsegmentations. Then, for the fusion of 3D CT and USimages, we present two approaches: (i) landmark-basedtransformation, and (ii) object-based transformation thatmakes use of eye ball contour information on CT and USimages.

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.

Mycobacterium tuberculosis-specific CD8 T cells are functionally and phenotypically different between latent infection and active disease

Purpose/Objective: Tuberculosis (TB) is the second worldwide leading cause of death from an infectious disease after HIV infection. Protective immunity to Mycobacterium tuberculosis (Mtb) remains poorly understood and the role of Mtb-specific CD8 T-cells is controversial. We performed comprehensive functional and phenotypic characterizations of Mtb-specific CD8 T-cell responses in 273 subjects with either latent Mtb infection (LTBI) or active TB disease (TB) to assess their profile and relevance in TB. Materials and methods: Using multi-parametric flow cytometry, we assessed Mtb-specific CD8 T-cell functional (production of IFNgamma, IL-2 and TNF-alpha; proliferation capacity and cytotoxicity) and phenotypic (T-cell differentiation and exhaustion) profiles in cells isolated from peripheral blood and correlated these profiles with distinct clinical presentations. Results: Mtb-specific CD8 T-cells were detected in most TB patients and few LTBI subjects (65% and 15%, respectively; P < 0.00001) and were of similar magnitude with a comparable cytokines profile (IFNg+TNFa+IL2-) in both groups. Mtb-specific CD8 T-cells were mostly TEMRA (CD45RA+ CCR7-) co-expressing 2B4 and CD160 in LTBI subjects and mostly TEM (CD45RA-CCR7-) lacking PD-1/ CD160/2B4 in TB patients. Furthermore, Mtb-specific CD8 T-cells mostly expressed very little perforin and granulysin but contained granzymes A and B or lacked all these cytotoxic markers in TB and LTBI subjects, respectively. However, in vitro expanded Mtb-specific CD8 T-cells acquired perforin, granulysin and granzymes. Finally, Mtb-specific CD8 T-cell responses were more robust and prone to proliferate in patients with extrapulmonary compared to pulmonary TB. Conclusions: The clinical status and TB presentation are associated to specific profiles of Mtb-specific CD8 T-cell responses, thus indicating distinct dynamics between the mycobacteria, the CD8 T-cell response and the clinical outcome. Our data shed light on the controversial reached by studies performed in human and animal models, thus advancing the current knowledge on the complex dynamic of TB immunity.

Illusory contours: a window onto the neurophysiology of constructing perception.

Seeing seems effortless, despite the need to segregate and integrate visual information that varies in quality, quantity, and location. The extent to which seeing passively recapitulates the external world is challenged by phenomena such as illusory contours, an example of visual completion whereby borders are perceived despite their physical absence in the image. Instead, visual completion and seeing are increasingly conceived as active processes, dependent on information exchange across neural populations. How this is instantiated in the brain remains controversial. Divergent models emanate from single-unit and population-level electrophysiology, neuroimaging, and neurostimulation studies. We reconcile discrepant findings from different methods and disciplines, and underscore the importance of taking into account spatiotemporal brain dynamics in generating models of brain function and perception.

Surveillance active du cancer de la prostate [Active surveillance in prostate cancer].

Active surveillance in prostate cancer The spread of PSA in the screening of prostate cancer has almost doubled the incidence of this disease in the last twenty years. An improved understanding of the natural history of this cancer allows for risk stratification of the disease and to better predict insignificant prostate cancer. Active surveillance has recently been proposed as a new option to delay or avoid a radical treatment for patients with low-risk disease. The principle, results and future perspectives of this treatment modality are discussed in this review.

Using active learning for monitoring networks design: The example of wind power plants sites evaluation

Locating new wind farms is of crucial importance for energy policies of the next decade. To select the new location, an accurate picture of the wind fields is necessary. However, characterizing wind fields is a difficult task, since the phenomenon is highly nonlinear and related to complex topographical features. In this paper, we propose both a nonparametric model to estimate wind speed at different time instants and a procedure to discover underrepresented topographic conditions, where new measuring stations could be added. Compared to space filling techniques, this last approach privileges optimization of the output space, thus locating new potential measuring sites through the uncertainty of the model itself.

Automatic Mallampati Classification Using Active Appearance Models

Difficult tracheal intubation assessment is an important research topic in anesthesia as failed intubations are important causes of mortality in anesthetic practice. The modified Mallampati score is widely used, alone or in conjunction with other criteria, to predict the difficulty of intubation. This work presents an automatic method to assess the modified Mallampati score from an image of a patient with the mouth wide open. For this purpose we propose an active appearance models (AAM) based method and use linear support vector machines (SVM) to select a subset of relevant features obtained using the AAM. This feature selection step proves to be essential as it improves drastically the performance of classification, which is obtained using SVM with RBF kernel and majority voting. We test our method on images of 100 patients undergoing elective surgery and achieve 97.9% accuracy in the leave-one-out crossvalidation test and provide a key element to an automatic difficult intubation assessment system.

Small ubiquitin-like modifier conjugating enzyme with active site mutation acts as dominant negative inhibitor of SUMO conjugation in arabidopsis(F).

Small ubiquitin-like modifier (SUMO) conjugation affects a broad range of processes in plants, including growth, flower initiation, pathogen defense, and responses to abiotic stress. Here, we investigate in vivo and in vitro a SUMO conjugating enzyme with a Cys to Ser change in the active site, and show that it has a dominant negative effect. In planta expression significantly perturbs normal development, leading to growth retardation, early flowering and gene expression changes. We suggest that the mutant protein can serve as a probe to investigate sumoylation, also in plants for which poor genetic infrastructure precludes analysis via loss-of-function mutants.