Search results for "Automated"
showing 10 items of 236 documents
3D segmentation of abdominal aorta from CT-scan and MR images
2012
International audience; We designed a generic method for segmenting the aneurismal sac of an abdominal aortic aneurysm (AAA) both from multi-slice MR and CT-scan examinations. It is a semi-automatic method requiring little human intervention and based on graph cut theory to segment the lumen interface and the aortic wall of AAAs. Our segmentation method works independently on MRI and CT-scan volumes and has been tested on a 44 patient dataset and 10 synthetic images. Segmentation and maximum diameter estimation were compared to manual tracing from 4 experts. An inter-observer study was performed in order to measure the variability range of a human observer. Based on three metrics (the maxim…
Managing Multi-center Flow Cytometry Data for Immune Monitoring.
2014
With the recent results of promising cancer vaccines and immunotherapy 1 – 5 , immune monitoring has become increasingly relevant for measuring treatment-induced effects on T cells, and an essential tool for shedding light on the mechanisms responsible for a successful treatment. Flow cytometry is the canonical multi-parameter assay for the fine characterization of single cells in solution, and is ubiquitously used in pre-clinical tumor immunology and in cancer immunotherapy trials. Current state-of-the-art polychromatic flow cytometry involves multi-step, multi-reagent assays followed by sample acquisition on sophisticated instruments capable of capturing up to 20 parameters per cell at a…
Automated Flow Cytometric Analysis of Blood Cells in Cerebrospinal Fluid
2004
We compared the performance of an automated method for obtaining RBC and WBC counts and WBC differential counts in cerebrospinal fluid (CSF) samples with the reference manual method. Results from 325 samples from 10 worldwide clinical sites were used to demonstrate the accuracy, precision, and linearity of the method. Accuracy statistics for absolute cell counts showed a high correlation between methods, with correlation coefficients for all reportable absolute counts greater than 0.9. Linearity results demonstrated that the method provides accurate results throughout the reportable ranges, including clinical decision points for WBCs of 0 to 10/μL. Interassay precision and intra-assay preci…
The structure of charoite, (K,Sr,Ba,Mn)(15-16)(Ca,Na)(32)[(Si-70(O,OH)(180))](OH,F)(4.0)center dot nH(2)O, solved by conventional and automated elect…
2010
AbstractCharoite, ideally (K,Sr,Ba,Mn)15–16(Ca,Na)32[(Si70(O,OH)180)](OH,F)4.0·nH2O, a rare mineral from the Murun massif in Yakutiya, Russia, was studied using high-resolution transmission electron microscopy, selected-area electron diffraction, X-ray spectroscopy, precession electron diffraction and the newly developed technique of automated electron-diffraction tomography. The structure of charoite (a= 31.96(6) Å,b= 19.64(4) Å,c= 7.09(1) Å, β = 90.0(1)°,V= 4450(24) Å3, space groupP21/m) was solvedab initioby direct methods from 2878 unique observed reflections and refined toR1/wR2= 0.17/0.21. The structure can be visualized as being composed of three different dreier silicate chains: a d…
Automatic detection of large dense-core vesicles in secretory cells and statistical analysis of their intracellular distribution.
2010
Analyzing the morphological appearance and the spatial distribution of large dense-core vesicles (granules) in the cell cytoplasm is central to the understanding of regulated exocytosis. This paper is concerned with the automatic detection of granules and the statistical analysis of their spatial locations in different cell groups. We model the locations of granules of a given cell as a realization of a finite spatial point process and the point patterns associated with the cell groups as replicated point patterns of different spatial point processes. First, an algorithm to segment the granules using electron microscopy images is proposed. Second, the relative locations of the granules with…
Pressurized liquid extraction of organic contaminants in environmental and food samples
2015
Pressurized liquid extraction (PLE) is an automated technique that uses elevated temperature and pressure to achieve exhaustive extraction from solid matrices, so reducing solvent consumption and enhancing sample throughput when compared with traditional procedures. Hence, it can be considered an environment-friendly technique, generating small volumes of waste and reducing costs and time. This review focuses on application of this green technique to the analysis of organic contaminants in food and environmental matrices for monitoring purposes. We examine fundamentals and key aspects of the development of a PLE method, including pressurized hot-water extraction, together with some relevant…
GenClust: A genetic algorithm for clustering gene expression data
2005
Abstract Background Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. Results GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, …
BELM: Bayesian Extreme Learning Machine
2011
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap…
Perceptual adaptive insensitivity for support vector machine image coding.
2005
Support vector machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant epsilon-insensitivity zone by Robinson and Kecman. However, according to the statistical properties of natural images and the properties of human perception, a constant insensitivity makes sense in the spatial domain but it is certainly not a good option in a frequency domain. In fact, in their approach, they made a fixed low-pass assumption as the number of discrete cosine transform (DCT) coefficients to be used in the training was limited. This paper extends the work of Robinson and Kecman by proposing the use of adaptive insensitivity SVMs [2] for image coding u…
Upport vector machines for nonlinear kernel ARMA system identification.
2006
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA 2k) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based syste…