Paintera

Description

Paintera is a general visualization tool for 3D volumetric data and proof-reading in segmentation/reconstruction with a primary focus on neuron reconstruction from electron micrographs in connectomics. It features/supports:

  •  Views of orthogonal 2D cross-sections of the data at arbitrary angles and zoom levels
  •  Mipmaps for efficient display of arbitrarily large data at arbitrary scale levels
  •  Label data
    •  Painting
    •  Manual agglomeration
    •  3D visualization as polygon meshes
      •  Meshes for each mipmap level
      •  Mesh generation on-the-fly via marching cubes to incorporate painted labels and agglomerations in 3D visualization. Marching Cubes is parallelized over small blocks. Only relevant blocks are considered (huge speed-up for sparse label data).

Paintera is implemented in Java and makes extensive use of the UI framework JavaFX

Paintera screenshot

TTK the Topology Toolkit

Description

The Topology ToolKit (TTK) is an open-source library and software collection for topological data analysis in scientific visualization.

TTK can handle scalar data defined either on regular grids or triangulations, either in 2D or in 3D. It provides a substantial collection of generic, efficient and robust implementations of key algorithms in topological data analysis. It includes:
 · For scalar data: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, topological simplification;
 · For bivariate scalar data: fibers, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces;
 · For uncertain scalar data: mandatory critical points;
 · For time-varying scalar data: critical point tracking;
 · For high-dimensional / point cloud data: dimension reduction;
 · and more!

 

TTK makes topological data analysis accessible to end users thanks to easy-to-use plugins for the visualization front end ParaView. Thanks to ParaView, TTK supports a variety of input data formats.
 

TTK is written in C++ but comes with a variety of bindings (VTK/C++, Python) and standalone command-line programs. It is modular and easy to extend. We have specifically developed it such that you can easily write your own data analysis tools as TTK modules.

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Paraview

Description

ParaView is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaView’s batch processing capabilities.

ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of petascale size as well as on laptops for smaller data, has become an integral tool in many national laboratories, universities and industry, and has won several awards related to high performance computation.

paraviewbloodcells

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

Creating an ImageJ plugin / command

Description

The best way to start writing an ImageJ2 plugin (ImageJ2 developers call it command and not plugin) is to download the example command from github and modify it. There is a video tutorial on the whole workflow on how to do this on youtube.

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scikit-learn (sklearn)

Description

Scikit-learn (sklearn) is a python library used for machine learning. sklearn contains simple and efficient tools for data mining and data analysis. Modules and functions include those for classification, regression, clustering, dimensionality reduction, model selection and data preprocessing. Many people have contributed to sklearn (list of authors)

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scikit-learn logo.

3-D Density Kernel Estimation

Description

3-D density kernel estimation (DKE-3-D) method, utilises an ensemble of random decision trees for counting objects in 3D images. DKE-3-D avoids the problem of discrete object identification and segmentation, common to many existing 3-D counting techniques, and outperforms other methods when quantification of densely packed and heterogeneous objects is desired. 

Find Maxima (Python)

Description

 

Maxima finding algorithm recreated from implementation in Fiji(ImageJ)

This is a re-implementation of the java plugin written by Michael Schmid and Wayne Rasband for ImageJ. The original java code source can be found in: https://imagej.nih.gov/ij/developer/source/ij/plugin/filter/MaximumFinder.java.html 

This implementation remains faithful to the original implementation but is not 100% optimised. The java version is faster but this could be alleviated by compiling c code for parts of the code. This script is simply to provide the functionality of the ImageJ find maxima algorithm to individuals writing pure python script.

find maxima comparison.

FoCuS-point

Description

FoCuS-point is stand-alone software for TCSPC correlation and analysis. FoCuS-point utilizes advanced time-correlated single-photon counting (TCSPC) correlation algorithms along with time-gated filtering and innovative data visualization. The software has been designed to be highly user-friendly and is tailored to handle batches of data with tools designed to process files in bulk. FoCuS-point also includes advanced diffusion curve fitting algorithms which allow the parameters of the correlation functions and thus the kinetics of diffusion to be established quickly and efficiently.

FoCuS-scan

Description

FoCuS-scan is software for processing and analysis of large-scale scanning fluorescence correlation spectroscopy (FCS) data. FoCuS-scan can correlate data acquired on conventional turn-key confocal systems and in the form of xt image carpets.