Automated Segmentation of Cerebral Vasculature with Aneurysms in 3DRA and TOF-MRA using Geodesic Active Regions: an Evaluation Study

Purpose: To evaluate the suitability of an improved version of an automatic segmentation method based on geodesic active regions (GAR) for segmenting cerebral vasculature with aneurysms from 3D X-ray reconstruc-tion angiography (3DRA) and time of °ight magnetic resonance angiography (TOF-MRA) images available in the clinical routine.Methods: Three aspects of the GAR method have been improved: execution time, robustness to variability in imaging protocols and robustness to variability in image spatial resolutions. The improved GAR was retrospectively evaluated on images from patients containing intracranial aneurysms in the area of the Circle of Willis and imaged with two modalities: 3DRA and TOF-MRA. Images were obtained from two clinical centers, each using di®erent imaging equipment. Evaluation included qualitative and quantitative analyses ofthe segmentation results on 20 images from 10 patients. The gold standard was built from 660 cross-sections (33 per image) of vessels and aneurysms, manually measured by interventional neuroradiologists. GAR has also been compared to an interactive segmentation method: iso-intensity surface extraction (ISE). In addition, since patients had been imaged with the two modalities, we performed an inter-modality agreement analysis with respect to both the manual measurements and each of the two segmentation methods. Results: Both GAR and ISE di®ered from the gold standard within acceptable limits compared to the imaging resolution. GAR (ISE, respectively) had an average accuracy of 0.20 (0.24) mm for 3DRA and 0.27 (0.30) mm for TOF-MRA, and had a repeatability of 0.05 (0.20) mm. Compared to ISE, GAR had a lower qualitative error in the vessel region and a lower quantitative error in the aneurysm region. The repeatabilityof GAR was superior to manual measurements and ISE. The inter-modality agreement was similar between GAR and the manual measurements. Conclusions: The improved GAR method outperformed ISE qualitatively as well as quantitatively and is suitable for segmenting 3DRA and TOF-MRA images from clinical routine.

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.

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Unsupervised active regions for multiresolution image segmentation

An unsupervised approach to image segmentation which fuses region and boundary information is presented. The proposed approach takes advantage of the combined use of 3 different strategies: the guidance of seed placement, the control of decision criterion, and the boundary refinement. The new algorithm uses the boundary information to initialize a set of active regions which compete for the pixels in order to segment the whole image. The method is implemented on a multiresolution representation which ensures noise robustness as well as computation efficiency. The accuracy of the segmentation results has been proven through an objective comparative evaluation of the method

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Unsupervised active regions for multiresolution image segmentation

An unsupervised approach to image segmentation which fuses region and boundary information is presented. The proposed approach takes advantage of the combined use of 3 different strategies: the guidance of seed placement, the control of decision criterion, and the boundary refinement. The new algorithm uses the boundary information to initialize a set of active regions which compete for the pixels in order to segment the whole image. The method is implemented on a multiresolution representation which ensures noise robustness as well as computation efficiency. The accuracy of the segmentation results has been proven through an objective comparative evaluation of the method

Narrow dust jets in a diffuse gas coma: a natural product of small active regions on comets

Comets often display narrow dust jets but more diffuse gas comae when their eccentric orbits bring them into the inner solar system and sunlight sublimates the ice on the nucleus. Comets are also understood to have one or more active areas covering only a fraction of the total surface active with sublimating volatile ices. Calculations of the gas and dust distribution from a small active area on a comet’s nucleus show that as the gas moves out radially into the vacuum of space it expands tangentially, filling much of the hemisphere centered on the active region. The dust dragged by the gas remains more concentrated over the active area. This explains some puzzling appearances of comets having collimated dust jets but more diffuse gaseous atmospheres. Our test case is 67P/Churyumov–Gerasimenko, the Rosetta mission target comet, whose activity is dominated by a single area covering only 4% of its surface.

Directional Geodesic Active Contours

We present a non-conformal metric that generalizes the geodesic active contours approach for image segmentation. The new metric is obtained by adding to the Euclidean metric an additional term that penalizes the misalignment of the curve with the image gradient and multiplying the resulting metric by a conformal factor that depends on the edge intensity. In this way, a closer fitting to the edge direction results. The provided experimental results address the computation of the geodesics of the new metric by applying a gradient descent to externally provided curves. The good performance of the proposed techniques is demonstrated in comparison with other active contours methods.

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.

The DART classification of unannotated transcription within the ENCODE regions: associating transcription with known and novel loci

For the ∼1% of the human genome in the ENCODE regions, only about half of the transcriptionally active regions (TARs) identified with tiling microarrays correspond to annotated exons. Here we categorize this large amount of “unannotated transcription.” We use a number of disparate features to classify the 6988 novel TARs—array expression profiles across cell lines and conditions, sequence composition, phylogenetic profiles (presence/absence of syntenic conservation across 17 species), and locations relative to genes. In the classification, we first filter out TARs with unusual sequence composition and those likely resulting from cross-hybridization. We then associate some of those remaining with proximal exons having correlated expression profiles. Finally, we cluster unclassified TARs into putative novel loci, based on similar expression and phylogenetic profiles. To encapsulate our classification, we construct a Database of Active Regions and Tools (DART.gersteinlab.org). DART has special facilities for rapidly handling and comparing many sets of TARs and their heterogeneous features, synchronizing across builds, and interfacing with other resources. Overall, we find that ∼14% of the novel TARs can be associated with known genes, while ∼21% can be clustered into ∼200 novel loci. We observe that TARs associated with genes are enriched in the potential to form structural RNAs and many novel TAR clusters are associated with nearby promoters. To benchmark our classification, we design a set of experiments for testing the connectivity of novel TARs. Overall, we find that 18 of the 46 connections tested validate by RT-PCR and four of five sequenced PCR products confirm connectivity unambiguously.

Reconstruction des mouvements du plasma dans une région active solaire à l'aide de données d'observation et d'une minimisation Lagrangienne

À ce jour, les différentes méthodes de reconstruction des mouvements du plasma à la surface du Soleil qui ont été proposées présupposent une MHD idéale (Welsch et al., 2007). Cependant, Chae & Sakurai (2008) ont montré l’existence d’une diffusivité magnétique turbulente à la photosphère. Nous introduisons une généralisation de la méthode du Minimum Energy Fit (MEF ; Longcope, 2004) pour les plasmas résistifs. Le Resistive Minimum Energy Fit (MEF-R ; Tremblay & Vincent, 2014) reconstruit les champs de vitesse du plasma et la diffusivité magnétique turbulente qui satisfont à l’équation d’induction magnétique résistive et qui minimisent une fonctionnelle analogue à l’énergie cinétique totale. Une séquence de magnétogrammes et de Dopplergrammes sur les régions actives AR 9077 et AR 12158 ayant chacune produit une éruption de classe X a été utilisée dans MEF-R pour reconstruire les mouvements du plasma à la surface du Soleil. Les séquences temporelles des vitesses et des diffusivités magnétiques turbulentes calculées par MEF-R sont comparées au flux en rayons X mous enregistré par le satellite GOES-15 avant, pendant et après l’éruption. Pour AR 12158, nous observons une corrélation entre les valeurs significatives de la diffusivité magnétique turbulente et de la vitesse microturbulente pour les champs magnétiques faibles.

Variegation of comet 67P/Churyumov-Gerasimenko in regions showing activity

Aims. We carried out an investigation of the surface variegation of comet 67P/Churyumov-Gerasimenko, the detection of regions showing activity, the determination of active and inactive surface regions of the comet with spectral methods, and the detection of fallback material. Methods. We analyzed multispectral data generated with Optical, Spectroscopic, and Infrared Remote Imaging System (OSIRIS) narrow angle camera (NAC) observations via spectral techniques, reflectance ratios, and spectral slopes in order to study active regions. We applied clustering analysis to the results of the reflectance ratios, and introduced the new technique of activity thresholds to detect areas potentially enriched in volatiles. Results. Local color inhomogeneities are detected over the investigated surface regions. Active regions, such as Hapi, the active pits of Seth and Ma'at, the clustered and isolated bright features in Imhotep, the alcoves in Seth and Ma'at, and the large alcove in Anuket, have bluer spectra than the overall surface. The spectra generated with OSIRIS NAC observations are dominated by cometary emissions of around 700 nm to 750 nm as a result of the coma between the comet's surface and the camera. One of the two isolated bright features in the Imhotep region displays an absorption band of around 700 nm, which probably indicates the existence of hydrated silicates. An absorption band with a center between 800-900 nm is tentatively observed in some regions of the nucleus surface. This absorption band can be explained by the crystal field absorption of Fe2+, which is a common spectral feature seen in silicates.

Microsensors technology used to measure productivity and other biochemical processes in the upper regions of aquatic sediments

Carbonate sediments are dynamic three-dimensional environments where the surface layers are constantly moving and mixing due to the energy of the water column. It is also an environment of dynamic biological, chemical and physical interaction and modification. The biological community can actively influence changes to sediment characteristics and associated biochemistry. Bioturbation resulting from macrofaunal activity disrupts sediment structure and biochemical arrangements and reduces the critical shear forces required to move sediment particles, adding to the dynamic and complex physical and biogeochemical nature of the sediment. Laboratory studies using both planner optodes and glass needle microsensors were used to measure abiotic sediment characteristics such as the depth distribution and concentrations of PAR. The biochemical nature of coral reef sediment were also investigated, specifically the quantification and the distribution of dissolved oxygen within coarse and fine-grained sediments under regimes of light and darkness. Results highlighted the significant contribution microalgal productivity and bioturbation has on distribution of dissolved oxygen in the upper sediment layers. On the reef flat a shallow water lander system was employed to measure concentrations of O2, pH, S, Ca and temperature over periods of 24 to 48 hours in coarse and fine-grained sediments. Similarities between laboratory and in situ results where evident, however the in situ environment was more dynamic and the distribution and concentrations of dissolved oxygen were more complex and correlated to irradiance, temperature and biological activity. Microsensor technology provides us with the opportunity to study, at very high resolutions, the upper irradiated; photosynthetically active regions of aquatic sediments along with anoxic processes deeper in sub-euphotic regions of the sediments.