Browse
Submit Data
Databases
API
Help

Dataset Information

16 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Autofocus method for automated microscopy using embedded GPUs.

ABSTRACT: In this paper we present a method for autofocusing images of sputum smears taken from a microscope which combines the finding of the optimal focus distance with an algorithm for extending the depth of field (EDoF). Our multifocus fusion method produces an unique image where all the relevant objects of the analyzed scene are well focused, independently to their distance to the sensor. This process is computationally expensive which makes unfeasible its automation using traditional embedded processors. For this purpose a low-cost optimized implementation is proposed using limited resources embedded GPU integrated on cutting-edge NVIDIA system on chip. The extensive tests performed on different sputum smear image sets show the real-time capabilities of our implementation maintaining the quality of the output image.

SUBMITTER: Castillo-Secilla JM

PROVIDER: S-EPMC5480576 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Autofocus method for automated microscopy using embedded GPUs.

Castillo-Secilla J M JM Saval-Calvo M M Medina-Valdès L L Cuenca-Asensi S S Martínez-Álvarez A A Sánchez C C Cristóbal G G

Biomedical optics express 20170222 3

In this paper we present a method for autofocusing images of sputum smears taken from a microscope which combines the finding of the optimal focus distance with an algorithm for extending the depth of field (EDoF). Our multifocus fusion method produces an unique image where all the relevant objects of the analyzed scene are well focused, independently to their distance to the sensor. This process is computationally expensive which makes unfeasible its automation using traditional embedded proces ...[more]

PMID: 28663861

Similar Datasets

High data output and automated 3D correlative light-electron microscopy method.

Project description:Correlative light/electron microscopy (CLEM) allows the simultaneous observation of a given subcellular structure by fluorescence light microscopy (FLM) and electron microscopy. The use of this approach is becoming increasingly frequent in cell biology. In this study, we report on a new high data output CLEM method based on the use of cryosections. We successfully applied the method to analyze the structure of rough and smooth Russell bodies used as model systems. The major advantages of our method are (i) the possibility to correlate several hundreds of events at the same time, (ii) the possibility to perform three-dimensional (3D) correlation, (iii) the possibility to immunolabel both endogenous and recombinantly expressed proteins at the same time and (iv) the possibility to combine the high data analysis capability of FLM with the high precision-accuracy of transmission electron microscopy in a CLEM hybrid morphometry analysis. We have identified and optimized critical steps in sample preparation, defined routines for sample analysis and retracing of regions of interest, developed software for semi/fully automatic 3D reconstruction and defined preliminary conditions for an hybrid light/electron microscopy morphometry approach.

| S-EPMC2635477 | biostudies-literature

A method for automated High throughput ChIPseq

Project description:Setup and optimisation of a high throughput pipeline for ChIPseq. The protocol used is ChIP carried out using the Agilent Bravo robot with subsequent Ilumina sequencing library preparation.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

2013-02-12 | E-ERAD-77 | biostudies-arrayexpress

Automated Cell Lineage Reconstruction using Label-Free 4D Microscopy.

Project description:Here we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time lapse imaging. embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance and generalizes well to images acquired in multiple labs on multiple instruments.

| S-EPMC10849476 | biostudies-literature

Correlative microscopy and block-face imaging (CoMBI) method for both paraffin-embedded and frozen specimens.

Project description:Correlative microscopy and block-face imaging (CoMBI), a method that we previously developed, is characterized by the ability to correlate between serial block-face images as 3-dimensional (3D) datasets and sections as 2-dimensional (2D) microscopic images. CoMBI has been performed for the morphological analyses of various biological specimens, and its use is expanding. However, the conventional CoMBI system utilizes a cryostat, which limits its compatibility to only frozen blocks and the resolution of the block-face image. We developed a new CoMBI system that can be applied to not only frozen blocks but also paraffin blocks, and it has an improved magnification for block-face imaging. The new system, called CoMBI-S, comprises sliding-type sectioning devices and imaging devices, and it conducts block slicing and block-face imaging automatically. Sections can also be collected and processed for microscopy as required. We also developed sample preparation methods for improving the qualities of the block-face images and 3D rendered volumes. We successfully obtained correlative 3D datasets and 2D microscopic images of zebrafish, mice, and fruit flies, which were paraffin-embedded or frozen. In addition, the 3D datasets at the highest magnification could depict a single neuron and bile canaliculus.

| S-EPMC8222340 | biostudies-literature

Accelerated 2D Classification With ISAC Using GPUs.

Project description:A widely used approach to analyze single particles in electron microscopy data is 2D classification. This process is very computationally expensive, especially when large data sets are analyzed. In this paper we present GPU ISAC, a newly developed, GPU-accelerated version of the established Iterative Stable Alignment and Clustering (ISAC) algorithm for 2D images and generating class averages. While the previously existing implementation of ISAC relied on a computer cluster, GPU ISAC enables users to produce high quality 2D class averages from large-scale data sets on a single desktop machine equipped with affordable, consumer-grade GPUs such as Nvidia GeForce GTX 1080 TI cards. With only two such cards GPU ISAC matches the performance of twelve high end cluster nodes and, using high performance GPUs, is able to produce class averages from a million particles in between six to thirteen hours, depending on data set quality and box size. We also show GPU ISAC to scale linearly in all input dimensions, and thereby capable of scaling well with the increasing data load demand of future data sets. Further user experience improvements integrate GPU ISAC seamlessly into the existing SPHIRE GUI, as well as the TranSPHIRE on-the-fly processing pipeline. It is open source and can be downloaded at https://gitlab.gwdg.de/mpi-dortmund/sphire/cuISAC/.

| S-EPMC9296836 | biostudies-literature

Automated counting of airborne asbestos fibers by a high-throughput microscopy (HTM) method.

Project description:Inhalation of airborne asbestos causes serious health problems such as lung cancer and malignant mesothelioma. The phase-contrast microscopy (PCM) method has been widely used for estimating airborne asbestos concentrations because it does not require complicated processes or high-priced equipment. However, the PCM method is time-consuming and laborious as it is manually performed off-site by an expert. We have developed a high-throughput microscopy (HTM) method that can detect fibers distinguishable from other spherical particles in a sample slide by image processing both automatically and quantitatively. A set of parameters for processing and analysis of asbestos fiber images was adjusted for standard asbestos samples with known concentrations. We analyzed sample slides containing airborne asbestos fibers collected at 11 different workplaces following PCM and HTM methods, and found a reasonably good agreement in the asbestos concentration. Image acquisition synchronized with the movement of the robotic sample stages followed by an automated batch processing of a stack of sample images enabled us to count asbestos fibers with greatly reduced time and labors. HTM should be a potential alternative to conventional PCM, moving a step closer to realization of on-site monitoring of asbestos fibers in air.

| S-EPMC3231659 | biostudies-literature

An embedded gene selection method using knockoffs optimizing neural network.

Project description:BackgroundGene selection refers to find a small subset of discriminant genes from the gene expression profiles. How to select genes that affect specific phenotypic traits effectively is an important research work in the field of biology. The neural network has better fitting ability when dealing with nonlinear data, and it can capture features automatically and flexibly. In this work, we propose an embedded gene selection method using neural network. The important genes can be obtained by calculating the weight coefficient after the training is completed. In order to solve the problem of black box of neural network and further make the training results interpretable in neural network, we use the idea of knockoffs to construct the knockoff feature genes of the original feature genes. This method not only make each feature gene to compete with each other, but also make each feature gene compete with its knockoff feature gene. This approach can help to select the key genes that affect the decision-making of neural networks.ResultsWe use maize carotenoids, tocopherol methyltransferase, raffinose family oligosaccharides and human breast cancer dataset to do verification and analysis.ConclusionsThe experiment results demonstrate that the knockoffs optimizing neural network method has better detection effect than the other existing algorithms, and specially for processing the nonlinear gene expression and phenotype data.

| S-EPMC7510330 | biostudies-literature

MetaBinG: using GPUs to accelerate metagenomic sequence classification.

Project description:Metagenomic sequence classification is a procedure to assign sequences to their source genomes. It is one of the important steps for metagenomic sequence data analysis. Although many methods exist, classification of high-throughput metagenomic sequence data in a limited time is still a challenge. We present here an ultra-fast metagenomic sequence classification system (MetaBinG) using graphic processing units (GPUs). The accuracy of MetaBinG is comparable to the best existing systems and it can classify a million of 454 reads within five minutes, which is more than 2 orders of magnitude faster than existing systems. MetaBinG is publicly available at http://cbb.sjtu.edu.cn/~ccwei/pub/software/MetaBinG/MetaBinG.php.

| S-EPMC3223155 | biostudies-literature

Automated single-cell motility analysis on a chip using lensfree microscopy.

Project description:Quantitative cell motility studies are necessary for understanding biophysical processes, developing models for cell locomotion and for drug discovery. Such studies are typically performed by controlling environmental conditions around a lens-based microscope, requiring costly instruments while still remaining limited in field-of-view. Here we present a compact cell monitoring platform utilizing a wide-field (24?mm(2)) lensless holographic microscope that enables automated single-cell tracking of large populations that is compatible with a standard laboratory incubator. We used this platform to track NIH 3T3 cells on polyacrylamide gels over 20?hrs. We report that, over an order of magnitude of stiffness values, collagen IV surfaces lead to enhanced motility compared to fibronectin, in agreement with biological uses of these structural proteins. The increased throughput associated with lensfree on-chip imaging enables higher statistical significance in observed cell behavior and may facilitate rapid screening of drugs and genes that affect cell motility.

| S-EPMC3989554 | biostudies-literature

Automated detection of neutropenia using noninvasive video microscopy of superficial capillaries.

Project description: Not available

| S-EPMC6684956 | biostudies-literature