472 research outputs found
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, . 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 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
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