Image visualisation

VTK is an open-source software system for image processing, 3D graphics, volume rendering and visualization. VTK includes many advanced algorithms (e.g., surface reconstruction, implicit modeling, decimation) and rendering techniques (e.g., hardware-accelerated volume rendering, LOD control).

VTK is used by academicians for teaching and research; by government research institutions such as Los Alamos National Lab in the US or CINECA in Italy; and by many commercial firms who use VTK to build or extend products.

The origin of VTK is with the textbook "The Visualization Toolkit, an Object-Oriented Approach to 3D Graphics" originally published by Prentice Hall and now published by Kitware, Inc. (Third Edition ISBN 1-930934-07-6). VTK has grown (since its initial release in 1994) to a world-wide user base in the commercial, academic, and research communities.

TissUUmaps is a browser-based tool for fast visualization and exploration of millions of data points overlaying a tissue sample. TissUUmaps can be used as a web service or locally in your computer, and allows users to share regions of interest and local statistics.

The Incucyte® Base Analysis Software provides a guided interface and purpose-built tools, which include the process of acquiring, viewing, analyzing and sharing images of living cells.

Orthanc aims at providing a simple, yet powerful standalone DICOM server. It is designed to improve the DICOM flows in hospitals and to support research about the automated analysis of medical images. Orthanc lets its users focus on the content of the DICOM files, hiding the complexity of the DICOM format and of the DICOM protocol.

Orthanc can turn any computer running Windows, Linux or OS X into a DICOM store (in other words, a mini-PACS system). Its architecture is lightweight and standalone, meaning that no complex database administration is required, nor the installation of third-party dependencies.

What makes Orthanc unique is the fact that it provides a RESTful API. Thanks to this major feature, it is possible to drive Orthanc from any computer language. The DICOM tags of the stored medical images can be downloaded in the JSON file format. Furthermore, standard PNG images can be generated on-the-fly from the DICOM instances by Orthanc.

Orthanc also features a plugin mechanism to add new modules that extends the core capabilities of its REST API. A Web viewer, a PostgreSQL database back-end, a MySQL database back-end, and a reference implementation of DICOMweb are currently freely available as plugins.

ModularImageAnalysis (MIA) is an ImageJ plugin which provides a modular framework for assembling image and object analysis workflows. Detected objects can be transformed, filtered, measured and related. Analysis workflows are batch-enabled by default, allowing easy processing of high-content datasets.

MIA is designed for “out-of-the-box” compatibility with spatially-calibrated 5D images, yielding measurements in both pixel and physical units.  Functionality can be extended both internally, via integration with SciJava’s scripting interface, and externally, with Java modules that extend the MIA framework. Both have full access to all objects and images in the analysis workspace.

Workflows are, by default, compatible with batch processing multiple files within a single folder. Thanks to Bio-Formats, MIA has native support for multi-series image formats such as Leica .lif and Nikon .nd2.

Workflows can be automated from initial image loading through processing, object detection, measurement extraction, visualisation, and data exporting. MIA includes near 200 modules integrated with key ImageJ plugins such as Bio-Formats, TrackMate and Weka Trainable Segmentation.

Module(s) can be turned on/off dynamically in response to factors such as availability of images and objects, user inputs and measurement-based filters. Switches can also be added to “processing view” for easy workflow control.

MIA is developed in the Wolfson Bioimaging Facility at the University of Bristol.

A collection of Java tools and HTTP services (APIs) for rendering transformed image tiles that includes:

• a JSON data model for render, tile, and transform specifications,
• a Java stand-alone render application / library,
• a set of render web services that provide RESTful APIs to persisted collections of tile and transform specifications, and
• a set of Java clients for processing/rendering data that utilize the render web services to retrieve and/or store persisted specifications.
• Docker packaging for building the libraries and deploying the render web services

The basic concept is to render images (tiles) based on transformation files, without having to store the big generated image from an alignment of tiles (mosaicking).

webKnossos is an open-source data sharing and annotation platform for tera-scale 2D and 3D image datasets.

The core features of webKnossos are:

• fast 3D data streaming
• share links to specific locations in the data
• uniquely fast skeleton annotation (flight mode) and
• efficient volume annotation
• mesh rendering
• collaboration and sharing tools

webKnossos facilitates image analysis workflows on multi-terabyte datasets, including visualization of raw and multi-modal microscopy data, distributed training data generation and proof-reading of automatic segmentation.

As a scientific resource, webknossos.org serves as a database for published image datasets including their annotations.

Viv is a JavaScript library providing utilities for rendering primary imaging data. Viv supports WebGL-based multi-channel rendering of both pyramidal and non-pyramidal images. The rendering components of Viv are provided as Deck.gl layers, facilitating image composition with existing layers and updating rendering properties within a reactive paradigm.

MoBIE (Multimodal Big Image Data Exploration) is a framework for sharing and interactive browsing of multimodal big image data. The MoBIE Fiji viewer is based on BigDataViewer and enables browsing of MoBIE datasets. 

It is also called Platybrowser, and uses the n5 format.

The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. Its graphical user interface is largely inspired from the open source software "ImageJ". ImageM can also be run on the open source alternative software to Matlab, Octave.

ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

MorphoNet is a novel concept of web-based morphodynamic browser to visualise and interact with complex datasets, with applications in research and teaching. 

MorphoNet offers a comprehensive palette of interactions to explore the structure, dynamics and variability of biological shapes and its connection to genetic expressions. 

By handling a broad range of natural or simulated morphological data, it fills a gap which has until now limited the quantitative understanding of morphodynamics and its genetic underpinnings by contributing to the creation of ever-growing morphological atlases.

Summary

napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (e.g. numpy, scipy). It includes critical viewer features out-of-the-box, such as support for large multi-dimensional data, and layering and annotation. By integrating closely with the Python ecosystem, napari can be easily coupled to leading machine learning and image analysis tools (e.g. scikit-image, scikit-learn, TensorFlow, PyTorch), enabling more user-friendly automated analysis.

Installation

• The installation procedure for Silicon Mac (M1 Processor, arm64 ) requires some tricks. As of Oct 2021, this procedure by Peter Sobolewski works but:
  • For installing pyqt5, use a slightly different command `brew install PyQt@5` to install PyQt5.  

Dragonfly is a software platform for the intuitive inspection of multi-scale multi-modality image data. Its user-friendly experience translates into powerful quantitative findings with high-impact visuals, driven by nuanced easy-to-learn controls.

For segmentation: It provides an engine fior machine Learning, Watershed and superpixel methods, support histological data .

It offers a 3D viewer, and python scripting capacities .

It is free for reserach use, but not for commercial usage.

Web based viewer developped for google for very big data: 

Neuroglancer is a WebGL-based viewer for volumetric data. It is capable of displaying arbitrary (non axis-aligned) cross-sectional views of volumetric data, as well as 3-D meshes and line-segment based models (skeletons). The segmentation has to be done before loading the dataset, it is not done Inside the viewer.

This is not an official Google product.

It has among other the nice feature of beeing able to generate url for sharing a specific view.

Note that the only supported browser for now are 

• Chrome >= 51
• Firefox >= 46

The macro generates orthogonal projections from bead images along the lateral and axial dimensions which are displayed using a customized look-up-table to color code intensities. A Gaussian curve is fit to the intensity profile of a fluorescent bead image and full-with-at-half-maximum (FWHM) values are extracted, and listed next to theoretical values for comparison. 

TEM ExosomeAnalyzer is a program for automatic and semi-automatic detection of extracellular vesicles (EVs), such as exosomes, or similar objects in 2D images from transmission electron microscopy (TEM). The program detects the EVs, finds their boundaries, and reports information about their size and shape.

The software has been developed in terms of project MUNI/M/1050/2013 and supported by Grant Agency of Masaryk University.

The EVs are detected based on the shape and edge contrast criteria. The exact shapes of the EVs are then segmented using a watershed-based approach.

With proper parameter settings, even images with EVs both lighter and darked than the background, or containing artifacts or precipitated stain can be processed. If the fully-automatic processing fails to produce the correct results, the program can be used semi-automatically, letting the user adjust the detection seeds during the intermediate steps, or even draw the whole segmentation manually.

FluoRender is an interactive rendering tool for confocal microscopy data visualization. It combines the rendering of multi-channel volume data and polygon mesh data, where the properties of each dataset can be adjusted independently and quickly. The tool is designed especially for neurobiologists, allowing them to better visualize confocal data from fluorescently-stained brains, but it is also useful for other biological samples.

3Dscript is a plugin for Fiji/ImageJ for creating 3D and 4D animations of microscope data. In contrast to existing 3D visualization packages, animations are not keyframe-based, but are described by a natural language-based syntax.

Labkit is an open-source tool to segment truly large image data using sparse training data. It has an intuitive and responsive user interface based on Big Data Viewer, allowing users to conveniently browse and annotate even terabyte sized image volumes.

Update site: Labkit

SciView is an ImageJ/FIJI plugin for 3D visualization of images and meshes. It uses the Scenery and ClearVolume infrastructure. SciView integrates ImageJ2 functionality, including ImageJ Ops and ImageJ Mesh, to provide the ability to interact with image and mesh data in 3D and interface with the popular Fiji software ecosystem.

An update site is available: http://sites.imagej.net/SciView/

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

shinyHTM is an open source, web-based tool for data exploration, image visualization and normalization of High Throughput Microscopy data. Within shinyHTM the user is guided through a linear workflow which follows the following best practices:

• Inspect the numerical data through plotting
• Measurements are linked to raw images
• Perform quality control to exclude images with aberrations or where image analysis failed
• Perform a reproducible data analysis
• Normalize data and report statistical significance

Image visualization relies on Fiji/ImageJ, along with its wealth of analytical tools.

shinyHTM can be used to analyze image features obtained with CellProfiler, ImageJ or any other bioimage analysis software. The output of analysis is a publication-ready scoring of the data.

shinyHTM is based on the R shiny package.