Automated Screening of Neurite Outgrowth

2003 ◽  
Vol 8 (1) ◽  
pp. 7-18 ◽  
Author(s):  
Peter Ramm ◽  
Yuriy Alexandrov ◽  
Andrzej Cholewinski ◽  
Yuriy Cybuch ◽  
Robert Nadon ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automated Analysis ◽  
Automated System ◽  
Neurite Length ◽  
Neurotrophic Activity ◽  
Manual Methods ◽  
Automated Screening ◽  
Analysis System ◽  
Automated Algorithms ◽  
Immunofluorescence Labeling

Outgrowth of neurites in culture is used for assessing neurotrophic activity. Neurite measurements have been performed very slowly using manual methods or more efficiently with interactive image analysis systems. In contrast, medium-throughput and noninteractive image analysis of neurite screens has not been well described. The authors report the performance of an automated image acquisition and analysis system (IN Cell Analyzer 1000) in the neurite assay. Neuro-2a (N2a) cells were plated in 96-well plates and were exposed to 6 conditions of retinoic acid. Immunofluorescence labeling of the cytoskeleton was used to detect neurites and cell bodies. Acquisition of the images was automatic. The image set was then analyzed by both manual tracing and automated algorithms. On 5 relevant parameters (number of neurites, neurite length, total cell area, number of cells, neurite length per cell), the authors did not observe a difference between the automated analysis and the manual analysis done by tracing. These data suggest that the automated system addresses the same biology as human scorers and with the same measurement precision for treatment effects. However, throughput of the automated system is orders of magnitude higher than with manual methods. ( Journal of Biomolecular Screening 2003:7-18)

scholarly journals A Simple, Flexible, Nonfluorescent System for the Automated Screening of Neurite Outgrowth

2005 ◽  
Vol 11 (2) ◽  
pp. 155-164 ◽  
Author(s):  
Raymond D. Price ◽  
Tomoya Oe ◽  
Takayuki Yamaji ◽  
Nobuya Matsuoka
Keyword(s):  
Neurite Outgrowth ◽  
Kinase Inhibitor ◽  
Rho Kinase ◽  
Automated Analysis ◽  
Automated System ◽  
Neurite Length ◽  
Multiple Parameters ◽  
Neurotrophic Activity ◽  
Automated Screening ◽  
Analysis System

Measurement of neurite outgrowth is a common assay of neurotrophic activity. However, currently available techniques for measuring neurite outgrowth are either time or resource intensive. The authors established a system in which chronic treatment of a subcloned SH-SY5Y cell line with aphidicolin and various concentrations of nerve growth factor (NGF) induced discernable alterations in proliferation and differentiation. Cells were fixed, labeled with a nonfluorescent dye, and evaluated both manually and with an automated analysis system. NGF increased multiple parameters of differentiation, including neurite length, the proportion of cells extending neurites, and branching, as well as promoting cellular survival/proliferation. Interestingly, although NGF treatment increased the total number of branches, it actually decreased the proportion of branches per neurite length. The authors observed no differences in results obtained using the manual and automated systems, but the automated system was orders of magnitude faster. To demonstrate the flexibility of the system, the authors also show that they could measure changes in differentiation induced by a small-molecule Rho kinase inhibitor, as well as by retinoic acid cotreatment with brain-derived neurotrophic factor. In addition to this flexibility, this system does not require specialized equipment or fluorescent antibodies for analysis and therefore provides a less resource-intensive alternative to fluorescence-based systems.

scholarly journals A comparison of the Mouse Song Analyzer and DeepSqueak ultrasonic vocalization analysis systems in C57BL/6J, FVB.129, and FVB mice

2021 ◽  
Author(s):  
Matthew S Binder ◽  
Zachary P Pranske ◽  
Joaquin N Lugo
Keyword(s):  
Fundamental Frequency ◽  
Vocal Communication ◽  
Automated Analysis ◽  
Ultrasonic Vocalization ◽  
Ultrasonic Vocalizations ◽  
Automated System ◽  
Two Systems ◽  
Similar Quantity ◽  
Analysis System

Vocal communication is an essential behavior in mammals and is relevant to human neurodevelopmental conditions. Mice produce communicative vocalizations, known as ultrasonic vocalizations (USVs), that can be recorded with various programs. The Mouse Song Analyzer is an automated analysis system, while DeepSqueak is a semi-automated system. We used data from C57BL/6J, FVB.129, and FVB mice to compare whether the DeepSqueak and Mouse Song Analyzer systems measure a similar total number, duration, and fundamental frequency of USVs. We found that the two systems detected a similar quantity of USVs for FVB.129 mice (r= .90, p< .001), but displayed lower correlations for C57BL/6J (r= .76, p< .001) and FVB mice (r= .60, p< .001). We also found that DeepSqueak detected significantly more USVs for C57BL/6J mice than the Mouse Song Analyzer. The two systems detected a similar duration of USVs for C57BL/6J (r= .82, p< .001), but lower correlations for FVB.129 (r= .13, p< .001) and FVB mice (r= .51, p< .01) were found, with DeepSqueak detecting significantly more USVs per each strain. We found lower than acceptable correlations for fundamental frequency in C57BL/6J (r= .54, p< .01), FVB.129 (r= .76, p< .001), and FVB mice (r= .07, p= .76), with the Mouse Song Analyzer detecting a significantly higher fundamental frequency for FVB.129 mice. These findings demonstrate that the strain of mouse used significantly affects the number, duration, and fundamental frequency of USVs that are detected between programs. Overall, we found that DeepSqueak is more accurate than the Mouse Song Analyzer.

scholarly journals Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

2006 ◽  
Vol 2006 ◽  
pp. 1-10
Author(s):  
S. Venkatraman ◽  
M. J. Doktycz ◽  
H. Qi ◽  
J. L. Morrell-Falvey
Keyword(s):  
Fluorescence Microscopy ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Biological Networks ◽  
Automated Analysis ◽  
Automated System ◽  
Positive Interactions ◽  
Protein Interaction Dataset ◽  
Analysis System ◽  
Microscopy Images

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction. Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.

scholarly journals Image analysis of bromodeoxyuridine (BrdU) staining for measurement of S-phase in rat and mouse liver.

1994 ◽  
Vol 42 (7) ◽  
pp. 939-944 ◽  
Author(s):  
A R Soames ◽  
D Lavender ◽  
J R Foster ◽  
S M Williams ◽  
E B Wheeldon
Keyword(s):  
Image Analysis ◽  
Mouse Liver ◽  
Form Factors ◽  
Automated Image Analysis ◽  
Automated System ◽  
Image Analysis System ◽  
Intense Staining ◽  
Wide Range ◽  
Analysis System ◽  
Brdu Staining

We developed a system for quantifying the numbers of bromodeoxyuridine (BrdU)-labeled hepatocyte nuclei in rat and mouse liver with an automated image analysis system. We began by developing a protocol for BrdU staining that would provide consistently intense staining to facilitate identification of both labeled and unlabeled nuclei by image analysis. Preliminary studies detected and characterized hepatocyte nuclei and differentiated them from non-hepatocyte nuclei using area and form factors. The parameters were selected to optimize discrimination between the two populations, selecting 90% of hepatocyte and 5% non-hepatocyte nuclei. Finally, we developed a program for automatic counting of BrdU-labeled hepatocyte and total hepatocyte nuclei. Results obtained from this method correlated well with data collected by a microscopist over a wide range of labeling indices. The automated system reduces interobserver variation and should minimize intraobserver error, as well as reducing the tedium of measuring labeling indices in the liver. Moreover, the techniques described should be applicable to other tissues.

Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

1977 ◽  
Vol 35 ◽  
pp. 220-221
Author(s):  
S.F. Stinson ◽  
J.C. Lilga ◽  
M.B. Sporn
Keyword(s):  
Image Analysis ◽  
Pattern Analysis ◽  
Relative Proportion ◽  
Automated Image Analysis ◽  
Image Analyzer ◽  
Cross Sectional ◽  
Exfoliated Cells ◽  
Neoplastic Lesions ◽  
Analysis System ◽  
Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

Chemical Classification and Particle Sizing of Foundry Sands

1985 ◽  
Vol 43 ◽  
pp. 388-389
Author(s):  
D.S. DeMiglio
Keyword(s):  
Image Analysis ◽  
Energy Dispersive Spectrometer ◽  
Image Analysis System ◽  
Representative Sampling ◽  
Foundry Sands ◽  
Large Atomic Number ◽  
Backscattered Electron ◽  
Sand Particles ◽  
Analysis System ◽  
Reclamation System

Much progress has been made in recent years towards the development of closed-loop foundry sand reclamation systems. However, virtually all work to date has determined the effectiveness of these systems to remove surface clay and metal oxide scales by a qualitative inspection of a representative sampling of sand particles. In this investigation, particles from a series of foundry sands were sized and chemically classified by a Lemont image analysis system (which was interfaced with an SEM and an X-ray energy dispersive spectrometer) in order to statistically document the effectiveness of a reclamation system developed by The Pangborn Company - a subsidiary of SOHIO.The following samples were submitted: unreclaimed sand; calcined sand; calcined & mechanically scrubbed sand and unused sand. Prior to analysis, each sample was sprinkled onto a carbon mount and coated with an evaporated film of carbon. A backscattered electron photomicrograph of a field of scale-covered particles is shown in Figure 1. Due to a large atomic number difference between sand particles and the carbon mount, the backscattered electron signal was used for image analysis since it had a uniform contrast over the shape of each particle.

A Review on Automatic Bill of Material Generation and Visual Inspection on PCBs

2019 ◽  
Author(s):  
Mukhil Azhagan M. S ◽  
Dhwani Mehta ◽  
Hangwei Lu ◽  
Sudarshan Agrawal ◽  
Mark Tehranipoor ◽  
...  
Keyword(s):  
Image Analysis ◽  
Visual Inspection ◽  
Automated System ◽  
Imaging Modalities ◽  
Future Research ◽  
Analysis Techniques ◽  
Bill Of Materials ◽  
Pros And Cons ◽  
Index Terms ◽  
Image Analysis Techniques

Abstract Globalization and complexity of the PCB supply chain has made hardware assurance a challenging task. An automated system to extract the Bill of Materials (BoM) can save time and resources during the authentication process, however, there are numerous imaging modalities and image analysis techniques that can be used to create such a system. In this paper we review different imaging modalities and their pros and cons for automatic PCB inspection. In addition, image analysis techniques commonly used for such images are reviewed in a systematic way to provide a direction for future research in this area. Index Terms—Component Detection, PCB, Authentication, Image Analysis, Machine Learning

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