About the Algebraic Solutions of Smallest Enclosing Cylinders Problems

Given n points in Euclidean space E^d, we propose an algebraic algorithm to compute the best fitting (d-1)-cylinder. This algorithm computes the unknown direction of the axis of the cylinder. The location of the axis and the radius of the cylinder are deduced analytically from this direction. Special attention is paid to the case d=3 when n=4 and n=5. For the former, the minimal radius enclosing cylinder is computed algebrically from constrained minimization of a quartic form of the unknown direction of the axis. For the latter, an analytical condition of existence of the circumscribed cylinder is given, and the algorithm reduces to find the zeroes of an one unknown polynomial of degree at most 6. In both cases, the other parameters of the cylinder are deduced analytically. The minimal radius enclosing cylinder is computed analytically for the regular tetrahedron and for a trigonal bipyramids family with a symmetry axis of order 3.Comment: 13 pages, 0 figure; revised version submitted to publication (previous version is a copy of the original one of 2010

Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-sequence Cardiac MR Images Segmentation

Analysis and modeling of the ventricles and myocardium are important in the diagnostic and treatment of heart diseases. Manual delineation of those tissues in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the boundaries makes the segmentation task rather challenging. Furthermore, the annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI, are often not available. We propose an end-to-end segmentation framework based on convolutional neural network (CNN) and adversarial learning. A dilated residual U-shape network is used as a segmentor to generate the prediction mask; meanwhile, a CNN is utilized as a discriminator model to judge the segmentation quality. To leverage the available annotations across modalities per patient, a new loss function named weak domain-transfer loss is introduced to the pipeline. The proposed model is evaluated on the public dataset released by the challenge organizer in MICCAI 2019, which consists of 45 sets of multi-sequence CMR images. We demonstrate that the proposed adversarial pipeline outperforms baseline deep-learning methods.Comment: 9 pages, 4 figures, conferenc

Dosage du carbone organique dissous dans les eaux douces naturelles. Intérêt, Principe, Mise en Oeuvre et Précautions Opératoires

International audienceCe mémoire présente un travail de validation du dosage du carbone organique dissous(COD) et du carbone inorganique (CI) contenus dans les eaux douces naturelles développé surl'analyseur Shimadzu©, modèle TOC 5050A.Les limites de détection calculées pour cet analyseur avec un catalyseur de sensibilitédite normale sont de 0.31 ppm pour le COD et de 0.10 ppm pour le CI, respectivement. Leslimites de quantification sont logiquement plus élevées: 0.41 ppm (COD) et 0.13 ppm (CI).Ces résultats permettent de conclure que le catalyseur dit de "sensibilité normale" qui équipeen routine cet analyseur est adapté à l'analyse de la plupart des eaux douces naturelles.Les tests d'étalonnage effectués ont montré que l'appareil Shimadzu© TOC 5050A étaittrès stable dans le temps, les dérives constatées étant inférieures à 5%. De ce fait, uneprocédure allégée d'étalonnage comprenant l'injection d'un seul point de gamme en début dechaque série d'échantillons suffit à garantir une bonne justesse des résultats, même si celle-ciest difficile à quantifier du fait de l'absence de solutions standard certifiées.Les trois méthodes susceptibles d'être mises en oeuvre par l'analyseur Shimadzu©, TOC5050A (COD = CT – CI; NPOC, NPIW) ont été testées. Seules la méthode COD = CT – CIpermet de doser la totalité du compartiment "matière organique dissoute" des eaux doucesnaturelles. Les deux autres ne permettent pas d'appréhender les molécules les plus volatiles,les écarts entre valeurs "vraies" et valeurs "mesurées" pouvant aller jusqu'à 25%.Des expériences visant à tester les modalités de préparation et de conservation deséchantillons ont également été effectuées. Les résultats montrent que pour des eaux peuchargées en matière organique (COD 5%) entre valeurs mesurées et valeurs vraies. Dans ce cas,l'opérateur soucieux de produire des résultats justes sera conduit à filtrer les eaux directementsur le terrain et à réduire au maximum l'intervalle de temps entre le prélèvement et l'analyse

Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning

Cardiovascular disease accounts for 1 in every 4 deaths in United States. Accurate estimation of structural and functional cardiac parameters is crucial for both diagnosis and disease management. In this work, we develop an ensemble learning framework for more accurate and robust left ventricle (LV) quantification. The framework combines two 1st-level modules: direct estimation module and a segmentation module. The direct estimation module utilizes Convolutional Neural Network (CNN) to achieve end-to-end quantification. The CNN is trained by taking 2D cardiac images as input and cardiac parameters as output. The segmentation module utilizes a U-Net architecture for obtaining pixel-wise prediction of the epicardium and endocardium of LV from the background. The binary U-Net output is then analyzed by a separate CNN for estimating the cardiac parameters. We then employ linear regression between the 1st-level predictor and ground truth to learn a 2nd-level predictor that ensembles the results from 1st-level modules for the final estimation. Preliminary results by testing the proposed framework on the LVQuan18 dataset show superior performance of the ensemble learning model over the two base modules.Comment: Jiasha Liu, Xiang Li and Hui Ren contribute equally to this wor

FastVentricle: Cardiac Segmentation with ENet

Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that post-processing of exams is tedious. Without automation, precise assessment of cardiac function via CMR typically requires an annotator to spend tens of minutes per case manually contouring ventricular structures. Automatic contouring can lower the required time per patient by generating contour suggestions that can be lightly modified by the annotator. Fully convolutional networks (FCNs), a variant of convolutional neural networks, have been used to rapidly advance the state-of-the-art in automated segmentation, which makes FCNs a natural choice for ventricular segmentation. However, FCNs are limited by their computational cost, which increases the monetary cost and degrades the user experience of production systems. To combat this shortcoming, we have developed the FastVentricle architecture, an FCN architecture for ventricular segmentation based on the recently developed ENet architecture. FastVentricle is 4x faster and runs with 6x less memory than the previous state-of-the-art ventricular segmentation architecture while still maintaining excellent clinical accuracy.Comment: 11 pages, 6 figures, Accepted to Functional Imaging and Modeling of the Heart (FIMH) 201

Automatic alignment of surgical videos using kinematic data

Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed and used in surgical and medical practice. Recently, surgical videos has been shown to provide a structure for peer coaching enabling novice trainees to learn from experienced surgeons by replaying those videos. However, the high inter-operator variability in surgical gesture duration and execution renders learning from comparing novice to expert surgical videos a very difficult task. In this paper, we propose a novel technique to align multiple videos based on the alignment of their corresponding kinematic multivariate time series data. By leveraging the Dynamic Time Warping measure, our algorithm synchronizes a set of videos in order to show the same gesture being performed at different speed. We believe that the proposed approach is a valuable addition to the existing learning tools for surgery.Comment: Accepted at AIME 201

The ECLAIRs micro-satellite mission for gamma-ray burst multi-wavelength observations

Gamma-ray bursts (GRB), at least those with a duration longer than a few seconds are the most energetic events in the Universe and occur at cosmological distances. The ECLAIRs micro-satellite, to be launched in 2009, will provide multi-wavelength observations of GRB, to study their astrophysics and to use them as cosmological probes. Furthermore in 2009 ECLAIRs is expected to be the only space borne instrument capable of providing a GRB trigger in near real-time with sufficient localization accuracy for GRB follow-up observations with the powerful ground based spectroscopic telescopes available by then. A "Phase A study" of the ECLAIRs project has recently been launched by the French Space Agency CNES, aiming at a detailed mission design and selection for flight in 2006. The ECLAIRs mission is based on a CNES micro-satellite of the "Myriade" family and dedicated ground-based optical telescopes. The satellite payload combines a 2 sr field-of-view coded aperture mask gamma-camera using 6400 CdTe pixels for GRB detection and localization with 10 arcmin precision in the 4 to 50 keV energy band, together with a soft X-ray camera for onboard position refinement to 1 arcmin. The ground-based optical robotic telescopes will detect the GRB prompt/early afterglow emission and localize the event to arcsec accuracy, for spectroscopic follow-up observations.Comment: 7 pages, 1 figure, proceedings of the conference "New Developments in Photodetection", Beaune (France), June 25005. Submitted to NIM-A (Elsevier Science

A high-pressure hydrogen time projection chamber for the MuCap experiment

The MuCap experiment at the Paul Scherrer Institute performed a high-precision measurement of the rate of the basic electroweak process of nuclear muon capture by the proton, $\mu^- + p \rightarrow n + \nu_\mu$. The experimental approach was based on the use of a time projection chamber (TPC) that operated in pure hydrogen gas at a pressure of 10 bar and functioned as an active muon stopping target. The TPC detected the tracks of individual muon arrivals in three dimensions, while the trajectories of outgoing decay (Michel) electrons were measured by two surrounding wire chambers and a plastic scintillation hodoscope. The muon and electron detectors together enabled a precise measurement of the $\mu p$ atom's lifetime, from which the nuclear muon capture rate was deduced. The TPC was also used to monitor the purity of the hydrogen gas by detecting the nuclear recoils that follow muon capture by elemental impurities. This paper describes the TPC design and performance in detail.Comment: 15 pages, 13 figures, to be submitted to Eur. Phys. J. A; clarified section 3.1.2 and made minor stylistic corrections for Eur. Phys. J. A requirement