2D

Schnitzcells is a MATLAB based software that allows for quantitative analysis of fluorescent time-lapse movies of living cells. The software package is developed most specifically for bacteria and has been instrumental in analyzing E.coli and B. subtilis movies. The software contains functions that segment cells (based on either fluorescence or phase images),tracks cells in a frame-to-frame manner,build lineage trees and quantitatively extracts fluorescence.

Strength: tools for manually editing segmentation and lineage, well documented, free matlab source code, sample data

Limitations: no support, changes need to be done directly in the matlab code

ilastik is a simple, user-friendly tool for interactive image classification, segmentation and analysis. It is built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi-automated object tracking, semi-automated segmentation and object counting without detection. Most analysis operations are performed lazily, which enables targeted interactive processing of data subvolumes, followed by complete volume analysis in offline batch mode. Using it requires no experience in image processing.

ilastik (the image learning, analysis, and segmentation toolkit) provides non-experts with a menu of pre-built image analysis workflows. ilastik handles data of up to five dimensions (time, 3D space, and spectral dimension). Its workflows provide an interactive experience to give the user immediate feedback on the quality of the results yielded by her chosen parameters and/or labelings.

The most commonly used workflow is pixel classification, which requires very little parameter tuning and instead offers a machine learning technique for segmenting an image based on local image features computed for each pixel.

Other workflows include:

Object classification: Similar to pixel classification, but classifies previously segmented objects by object characteristics in a subsequent step

Autocontext: This workflow improves the pixel classification workflow by running it in multiple stages and showing each pixel the results of the previous stage.

Carving: Semi-automated segmentation of 3D objects (e.g. neurons) based on user-provided seeds

Manual Tracking: Semi-automated cell tracking of 2D+time or 3D+time images based on manual annotations

Automated tracking: Fully-automated cell tracking of 2D+time or 3D+time images with some parameter tuning

Density Counting: Learned cell population counting based on interactively provided user annotation

Strengths: interactive, simple interface (for non-experts), few parameters, larger-than-RAM data, multi-dimensional data (time, 3D space, channel), headless operation, batch mode, parallelized computation, open source

Weaknesses: Pre-built workflows (not reconfigurable), no plugin system, visualization sometimes buggy, must import 3D data to HDF5, tracking requires an external CPLEX installation

Supported Formats: hdf5, tiff, jpeg, png, bmp, pnm, gif, hdr, exr, sif

Reproducing an experiment doesn’t stop at the bench when images are concerned. Icy is an open source bioimaging software package that aims to provide a framework for authors to share, and others to reproduce, research once the sample hits the microscope. Icy was released in April 2011 and is being developed at the Quantitative Image Analysis Unit at the Pasteur Institute in France by Jean-Christophe Olivo-Marin and his team. The goal is to provide standardized software architecture, with a visual programming framework and online repository of plugins and protocols, brought together with sophisticated content-management and communication systems for such extended reproducible research. Icy provides intuitive user interfaces for graphical protocol development for image acquisition, analysis and storage that are easy to use for biologists and developers alike. Developers should find that Icy’s ‘EzPlug’ API library, versioning, and auditing tools make creating a custom plugin from most any source easy. Users will find the automatic error reporting, central repository and on-line community hub great for storing and sharing plugins and protocols. Icy is even developing a cloud-computing framework to address the scalability issues of high-content screening. As of this writing there are 207 plug-ins 50 scripts and 14 protocols available for download, including those for microscope control, particle tracking, three dimensional segmentation, and even spot detection using wavelets.

Published in Nature Methods (Nat Methods 9(7):690-6 (2012)). Icy can be downloaded at http://icy.bioimageanalysis.org/ Strength: Open-source. Centralized repository of 205 plugins, 50 scripts and 14 protocols

Rate and comment plugins 5D Search and install features directly from Icy Graphical programming with protocols Write scripts in javascript or python Automatic bug reports Native ImageJ integration 100% compatible Native Micro-Manager integration Share your plugins and protocols online Can run headless Intuitive user interface Online management of plugins Connect Icy to Matlab Interactive widgets Build your graphical interface with EzPlug Use the power of your graphic card with OpenCL Loaded with 20 up-to date libs Weaknesses No tutorial for plugins writing..yet See here: http://icy.bioimageanalysis.org/index.php?display=devDoc http://icy.bioimageanalysis.org/index.php?display=detailTag&tagId=29 and here: http://icy.bioimageanalysis.org/index.php?display=startDevWithIcy and also here: http://icy.bioimageanalysis.org/index.php?display=startDevWithIcy Image size limited to 2GigaByte per single 2D channel (means that an image of 40.000x40.000 can be handle by Icy. Still big !) Still you can have a stack of 100000x40Kx40kxUnlimited number of channel if you have RAM. Will be improved

ImgLib2 is a generic, multi-dimensional data processing library allowing for processing algorithms to be defined in a data-type, dimension, and container independent manner. Due to its interface-based design, it is easy to write adapters to virtually all existing data containers. It is the basis of KNIME, ImageJ2 and a couple of Fiji plugins.

Fiji is just ImageJ: a distribution of ImageJ (and ImageJ2) together with Java, Java 3D and a lot of plugins organized into a coherent menu structure. The main focus of Fiji is to assist research in life sciences. It is a free, open-source, community-driven project.