Synonyms
HCS

FishInspector

Description

The software FishInspector provides automatic feature detections in images of zebrafish embryos (body size, eye size, pigmentation). It is Matlab-based and provided as a Windows executable (no matlab installation needed).

The recent version requires images of a lateral position. It is important that the position is precise since deviation may confound with feature annotations. Images from any source can be used. However, depending on the image properties parameters may have to be adjusted. Furthermore, images obtained with normal microscope and not using an automated position system with embryos in glass capillaries require conversion using a KNIME workflow (the workflow is available as well). As a result of the analysis the software provides JSON files that contain the coordinates of the features. Coordinates are provided for eye, fish contour, notochord , otoliths, yolk sac, pericard and swimbladder. Furthermore, pigment cells in the notochord area are detected. Additional features can be manually annotated. It is the aim of the software to provide the coordinates, which may then be analysed subsequently to identify and quantify changes in the morphology of zebrafish embryos.

FishInspector Logo

acquiarium

Description

Acquiarium is open source software (GPL) for carrying out the common pipeline of many spatial cell studies using fluorescence microscopy. It addresses image capture, raw image correction, image segmentation, quantification of segmented objects and their spatial arrangement, volume rendering, and statistical evaluation.

It is focused on quantification of spatial properties of many objects and their mutual spatial relations in a collection of many 3D images. It can be used for analysis of a collection of 2D images or time lapse series of 2D or 3D images as well. It has a modular design and is extensible via plug-ins. It is a stand-alone, easy to install application written in C++ language. The GUI is written using cross-platform wxWidgets library.

Acquiarium functionalities diagram

ZEN Intellesis Trainable Segmentation

Description

Perform Advanced Image Segmentation and Processing across Microscopy Methods
 

Overcome the bottleneck of segmenting your Materials Science images and use ZEISS ZEN Intellesis, a module of the digital imaging software ZEISS ZEN.
Independent of the microscope you used to acquire your image data, the algorithm of ZEN Intellesis will provide you with a model for automated segmentation after training. Reuse the model on the same kind of data and beneft from consistent and repeatable segmentation, not influenced by the operator. 
ZEN Intellesis offers a straightforward, ease-to-use workflow that enables every microscope user to perform advanced segmentation tasks rapidly.

Highlights

  • Simple User Interface for Labelling and Training
  • Integration into ZEN Measurement Framework
  • Support for Multi-dimensional Datasets
  • Use powerful machine learning algorithms for pixel-based classifcation
  • Real Multi-Channel Feature Extraction
  • Engineered Feature Set and Deep Feature Extraction on GPU
  • IP-Function for creating masks an OAD-enabled for advanced automation
  • Powered by ZEN and Python3 using Anaconda Python Distribution
  • Just label objects, train your model and segment your images – there is no need for expert image analysis skills
  • Segment any kind of image data in 2D or 3D. Use data from light, electron, ion or x-ray microscopy, or your mobile phone
  • Speed up your segmentation task by built-in parallelization and GPU (graphics processing unit) acceleration
  • Increase tolerance to low signal-to-noise and artifact-ridden data
  • Seamless integration in ZEN framework and image analysis wizard
  • Data agnostic
  • Compatibility with 2D, 3D and up to 6D datasets
  • Export of multi-channel or labeled images
  • Exchange and sharing of models
  • GPU computing
  • Large data handling
  • Common and well-established machine learning algorithms
  • SW Trial License available

Fit a model for the growth of yeast cells

Description

This notebook uses the rOMERO-gateway and EBImage to process an Image associated to the paper 'Timing of gene expression in a cell-fate decision system'.

The Image "Pos22" is taken from the dataset idr0040-aymoz-singlecell/experimentA/YDA306_AGA1y_PRM1r_Mating. It is a timelapse Image with 42 timepoints separated by 5 minutes. This Image is used to fit a model for the growth of the yeast cells. The notebook does not replicate any of the analysis of the above mentioned paper.

Its purpose is mainly to demonstrate the use of Jupyter, rOMERO-gateway and EBimage.

 

What it does:

  • For each time point of one movie:
    • Read the image for this time point  from the IDR
    • Threshold the images and count the cells using EBimage functions
  • Fit an exponential model to the count of cells against time to get a coefficient of grow (exponential factor)

 

 

 

has function

Quantification of outer ring diameters of centriole or PCM proteins of cycling HeLa cells in interphase

Description

This workflow can be ran with data from 3D-SIM showing the centrosomes in order to compare the distribution of diameters of rings (or toroids) of different proteins from the centrioles or the peri centriolar material. It aims to reproduce the results of the Nature Cell Biology Paper Subdiffraction imaging of centrosomes reveals higher-order organizational features of pericentriolar material  from the same data set but with a different analysis method.

It is slightly different from the methods described in the paper itself, where the method was to work on a maximum intensity projection of a 3D-SIM stack, and then to fit circle to the centrioles to estimate the diameters of the toroids.

In this workflow, the images are read from the IDR , then process by thresholding (Maximum entropy auto thresholding with Image J), and processed by Analyze Particles  with different measurement sets, including the bouding box. Then the analysis of diameters and the statistical test are performed using R. All the code and data sets are available, and in the case of this paper have shown a layered organisation of the proteins.

Combined view from Figure 1 Lawo et al.

Bioconductor

Description

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. It has two releases each year, 1560 software packages, and an active user community. Bioconductor is also available as an AMI (Amazon Machine Image) and a series of Docker images.

has function

CEM

Description

Computer-assisted Evaluation of Myelin formation (CEM) is a collection designed to automate myelin quantification. It requires use input to choose the best threshold values. The myelin is calculated as an overlap between neuronal signal and oligodendrocyte signal. Results are given as pixel counts and percents.

CEM runs as an imageJ plugin with an optional Matlab extension to remove cell bodies. More details are published at Kerman et al. 2015 Development. Supplemental Material includes a detailed user manual and the download link.

Myelin

EBImage

Description

EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data.

EBImage is available through the Bioconductor software project (www.bioconductor.org). Strengths Lightweight Suitable for automated, scripted analyses All functions are documented with examples Modular links to R and Bioconductor software, notably imageHTS and cellHTS2 Community support via the Bioconductor mailing list Reproducible (image) analysis using the Sweave report-writing system

EBImage

CIDRE

Description

CIDRE is a retrospective illumination correction method for optical microscopy. It is designed to correct collections of images by building a model of the illumination distortion directly from the image data. Larger image collections provide more robust corrections. Details of the method are described in

K. Smith, Y. Li, F. Ficcinini, G. Csucs, A. Bevilacqua, and P. Horvath
CIDRE: An Illumination Correction Method for Optical Microscopy, Nature Methods 12(5), 2015, doi:10.1038/NMETH.3323

Illumination correction method