Automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound

Transperineal volumetric ultrasound (US) imaging has become routine practice for diagnosing pelvic floor disease (PFD). Hereto, clinical guidelines stipulate to make measurements in an anatomically defined 2D plane within a 3D volume, the so-called C-plane. This task is currently performed manually in clinical practice, which is labour-intensive and requires expert knowledge of pelvic floor anatomy, as no computer-aided C-plane method exists. To automate this process, we propose a novel, guideline-driven approach for automatic detection of the C-plane. The method uses a convolutional neural network (CNN) to identify extreme coordinates of the symphysis pubis and levator ani muscle (which define the C-plane) directly via landmark regression. The C-plane is identified in a postprocessing step. When evaluated on 100 US volumes, our best performing method (multi-task regression with UNet) achieved a mean error of 6.05 mm and 4.81 ∘ and took 20 s. Two experts blindly evaluated the quality of the automatically detected planes and manually defined the (gold standard) C-plane in terms of their clinical diagnostic quality. We show that the proposed method performs comparably to the manual definition. The automatic method reduces the average time to detect the C-plane by 100 s and reduces the need for high-level expertise in PFD US assessment

First Results of the Phase II SIMPLE Dark Matter Search

We report results of a 14.1 kgd measurement with 15 superheated droplet detectors of total active mass 0.208 kg, comprising the first stage of a 30 kgd Phase II experiment. In combination with the results of the neutron-spin sensitive XENON10 experiment, these results yield a limit of |a_p| < 0.32 for M_W = 50 GeV/c2 on the spin-dependent sector of weakly interacting massive particle-nucleus interactions with a 50% reduction in the previously allowed region of the phase space formerly defined by XENON, KIMS and PICASSO. In the spin-independent sector, a limit of 2.3x10-5 pb at M_W = 45 GeV/c2 is obtained.Comment: 4 pages, 4 figures; PRL-accepted version with corrected SI contour (Fig. 4

Automatic Extraction of Hiatal Dimensions in 3-D Transperineal Pelvic Ultrasound Recordings

The aims of this work were to create a robust automatic software tool for measurement of the levator hiatal area on transperineal ultrasound (TPUS) volumes and to measure the potential reduction in variability and time taken for analysis in a clinical setting. The proposed tool automatically detects the C-plane (i.e., the plane of minimal hiatal dimensions) from a 3-D TPUS volume and subsequently uses the extracted plane to automatically segment the levator hiatus, using a convolutional neural network. The automatic pipeline was tested using 73 representative TPUS volumes. Reference hiatal outlines were obtained manually by two experts and compared with the pipeline's automated outlines. The Hausdorff distance, area, a clinical quality score, C-plane angle and C-plane Euclidean distance were used to evaluate C-plane detection and quantify levator hiatus segmentation accuracy. A visual Turing test was created to compare the performance of the software with that of the expert, based on the visual assessment of C-plane and hiatal segmentation quality. The overall time taken to extract the hiatal area with both measurement methods (i.e., manual and automatic) was measured. Each metric was calculated both for computer–observer differences and for inter-and intra-observer differences. The automatic method gave results similar to those of the expert when determining the hiatal outline from a TPUS volume. Indeed, the hiatal area measured by the algorithm and by an expert were within the intra-observer variability. Similarly, the method identified the C-plane with an accuracy of 5.76 ± 5.06° and 6.46 ± 5.18 mm in comparison to the inter-observer variability of 9.39 ± 6.21° and 8.48 ± 6.62 mm. The visual Turing test suggested that the automatic method identified the C-plane position within the TPUS volume visually as well as the expert. The average time taken to identify the C-plane and segment the hiatal area manually was 2 min and 35 ± 17 s, compared with 35 ± 4 s for the automatic result. This study presents a method for automatically measuring the levator hiatal area using artificial intelligence-based methodologies whereby the C-plane within a TPUS volume is detected and subsequently traced for the levator hiatal outline. The proposed solution was determined to be accurate, relatively quick, robust and reliable and, importantly, to reduce time and expertise required for pelvic floor disorder assessment

Undocumented Migrants in Switzerland: Geographical Origin Versus Legal Status as Risk Factor for Tuberculosis

Undocumented migrants, meaning migrants without a legal residency permit, come to Geneva from countries with high tuberculosis (TB) incidence. We estimate here whether being undocumented is a determinant of TB, independently of origin. Cross-sectional study including undocumented migrants in a TB screening program in 2002; results were compared to 12,904 age and frequency matched participants in a general TB screening program conducted at various workplaces in Geneva, Switzerland from 1992 to 2002. A total of 206 undocumented migrants (36% male, 64% female, mean age 37.8 years (SD 11.8), 82.5% from Latin America) participated in the TB screening program. Compared to legal residents, undocumented migrants had an adjusted OR for TB-related fibrotic signs of 1.7 (95% CI 0.8;3.7). The OR of TB-related fibrotic signs for Latin American (vs. other) origin was 2.7 (95% CI 1.6;4.7) among legal residents and 5.5 (95% CI 2.8;10.8) among undocumented migrants. Chest X-ray screening identified a higher proportion of TB-related fibrotic signs among Latin Americans, independently of their residency statu

Final Analysis and Results of the Phase II SIMPLE Dark Matter Search

We report the final results of the Phase II SIMPLE measurements, comprising two run stages of 15 superheated droplet detectors each, the second stage including an improved neutron shielding. The analyses includes a refined signal analysis, and revised nucleation efficiency based on reanalysis of previously-reported monochromatic neutron irradiations. The combined results yield a contour minimum of \sigma_{p} = 4.2 x 10^-3 pb at 35 GeV/c^2 on the spin-dependent sector of WIMP-proton interactions, the most restrictive to date from a direct search experiment and overlapping for the first time results previously obtained only indirectly. In the spin-independent sector, a minimum of 3.6 x 10^-6 pb at 35 GeV/c^2 is achieved, with the exclusion contour challenging the recent CoGeNT region of current interest.Comment: revised, PRL-accepted version with slightly weakened limit contour

Automated white matter hyperintensity segmentation using Bayesian Model Selection: assessment and correlations with cognitive change

Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal cognitive change in control, early Mild Cognitive Impairment (EMCI), late Mild Cognitive Impairment (LMCI), subjective/significant memory concern (SMC) and Alzheimer’s (AD) participants. Data were downloaded from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Magnetic resonance images from 30 control and 30 AD participants were selected to incorporate multiple scanners, and were semi-automatically segmented by 4 raters and BaMoS. Segmentations were assessed using volume correlation, Dice score, and other spatial metrics. Linear mixed-effect models were fitted to 180 control, 107 SMC, 320 EMCI, 171 LMCI and 151 AD participants separately in each group, with the outcomes being cognitive change (e.g. mini-mental state examination; MMSE), and BaMoS WMH, age, sex, race and education used as predictors. There was a high level of agreement between BaMoS’ WMH segmentation volumes and a consensus of rater segmentations, with a median Dice score of 0.74 and correlation coefficient of 0.96. BaMoS WMH predicted cognitive change in: control, EMCI, and SMC groups using MMSE; LMCI using clinical dementia rating scale; and EMCI using Alzheimer’s disease assessment scale-cognitive subscale (p < 0.05, all tests). BaMoS compares well to semi-automated segmentation, is robust to different WMH loads and scanners, and can generate volumes which predict decline. BaMoS can be applicable to further large-scale studies

The mammal localities of Grand Daoui quarries, Ouled Abdoun Basin, Morroco, Ypresian : a first survey

Nos recherches de terrain permettent de restituer le contexte géologique et stratigraphique d’origine des localités à Phosphatherium escuilliei (Proboscidea) du bassin à phosphates des Ouled Abdoun au Maroc. Le matériel de P. escuilliei et de plusieurs autres mammifères provient des exploitations de Grand Daoui (NE du bassin). Nos observations montrent que leur niveau d’origine est un bone-bed situé à la base de l’Intercalaire Couche II/I daté classiquement de l’Yprésien inférieur. Ces niveaux du début de l’Eocène marquent l’installation d’une nouvelle mégaséquence dans la série phosphatée qui a partiellement remanié les niveaux sous-jacents du Thanétien. Cela explique la présence de sélaciens du Thanétien dans la gangue de P. escuilliei et sa datation initiale du Paléocène. L’étude des sélaciens associés au nouveau matériel mammalien récolté confirme l’âge éocène basal de P. escuilliei avec l’identification de plusieurs espèces de l’Yprésien inférieur. La présence exceptionnelle de mammifères dans les Ouled Abdoun dénote d’une taphonomie particulière des restes continentaux dans les dépôts phosphatés. Ceux-ci ont été transportés dans le bassin depuis l’arrière-pays continental proche dans des conditions de faible énergie hydrodynamique, probablement sous forme de cadavres flottés. Au total le matériel récolté dans les carrières de Grand Daoui documente 7 espèces de mammifères dont un créodonte, deux proboscidiens, deux « condylarthres », et deux espèces indéterminées décrites ici. L’une est un des plus anciens hyracoïdes signalés. P. escuilliei est très largement prédominant parmi ces mammifères. La faune de vertébrés qui leur est associée comprend des restes d’oiseaux signalés pour la première fois dans les bassins à phosphates d’Afrique. Ce sont les plus anciens oiseaux connus en Afrique et parmi les premiers représentants des groupes marins modernes

Top marine predators track Lagrangian coherent structures

Meso- and submesoscales (fronts, eddies, filaments) in surface ocean flow have a crucial influence on marine ecosystems. Their dynamics partly control the foraging behaviour and the displacement of marine top predators (tuna, birds, turtles, and cetaceans). In this work we focus on the role of submesoscale structures in the Mozambique Channel on the distribution of a marine predator, the Great Frigatebird. Using a newly developed dynamical concept, namely the Finite-Size Lyapunov Exponent (FSLE), we have identified Lagrangian coherent structures (LCSs) present in the surface flow in the Channel over a 2-month observation period (August and September 2003). By comparing seabirds' satellite positions with LCSs locations, we demonstrate that frigatebirds track precisely these structures in the Mozambique Channel, providing the first evidence that a top predator is able to track these FSLE ridges to locate food patches. After comparing bird positions during long and short trips, and different parts of these trips, we propose several hypotheses to understand how frigatebirds can follow these LCSs. The birds might use visual and/or olfactory cues and/or atmospheric current changes over the structures to move along these biological corridors. The birds being often associated to tuna schools around foraging areas, a thorough comprehension of their foraging behaviour and movement during the breeding season is crucial not only to seabirds' ecology but also to an appropriate ecosystemic approach of fisheries in the Channel

ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification and regression problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait. At the same time, predicting class relevance from brain images is challenging as phenotypes are typically heterogeneous, and changes occur against a background of significant natural variation. Here, we present an extension of the ICAM framework for creating prediction specific FA maps through image-to-image translation