Cell segmentation

Orbit Image Analysis is a free open source software with the focus to quantify big images like whole slide scans.

It can connect to image servers, e.g. Omero.
Analysis can be done on your local computer or via scaleout functionality in a distrubuted computing environment like a Spark cluster.

Sophisticated image analysis algorithms incl. tissue quantification using machine learning, object segmentation and classification are build in. In addition a versatile API allows you to enhance Orbit and to run your own scripts.

automated open-source image acquisition and on-the-fly analysis pipeline (initially developped for analysis of mitotic defects in fission yeast)

The Adipocytes Tools help to analyze fat cells in images from histological section. This is a rather general cell segmentation approach. It can be adapted to different situations via the parameters. This means that you have to find the right parameters for your application.

Sample Image: [0178_x5_3.tif](http://dev.mri.cnrs.fr/attachments/190/0178_x5_3.tif)

Analysis of adipocyte number and size. The original code and example images supposed to be discovered at http://webspace.buckingham.ac.uk/klanglands/ but currently the webpage is missing the code and sample images.

Vaa3D is a handy, fast, and versatile 3D/4D/5D Image Visualization and Analysis System for Bioimages and Surface Objects. It also provides many unique functions that you may not find in other software. It is Open Source, and supports a very simple and powerful plugin interface and thus can be extended and enhanced easily.

Vaa3D is cross-platform (Mac, Linux, and Windows). This software suite is powerful for visualizing large- or massive-scale (giga-voxels and even tera-voxels) 3D image stacks and various surface data. Vaa3D is also a container of powerful modules for 3D image analysis (cell segmentation, neuron tracing, brain registration, annotation, quantitative measurement and statistics, etc) and data management. This makes Vaa3D suitable for various bioimage informatics applications, and a nice platform to develop new 3D image analysis algorithms for high-throughput processing. In short, Vaa3D streamlines the workflow of visualization-assisted analysis.

Vaa3D can render 5D (spatial-temporal) data directly in 3D volume-rendering mode; it supports convenient and interactive local and global 3D views at different scales... it comes with a number of plugins and toolboxes. Importantly, you can now write your own plugins to take advantage of the Vaa3D platform, possibly within minutes!

Summary

QuimP is software for tracking cellular shape changes and dynamic distributions of fluorescent reporters at the cell membrane. QuimP's unique selling point is the possibility to aggregate data from many cells in form of spatio-temporal maps of dynamic events, independently of cell size and shape. QuimP has been successfully applied to address a wide range of problems related to cell movement in many different cell types. 

Introduction

In transmembrane signalling the cell membrane plays a fundamental role in localising intracellular signalling components to specific sites of action, for example to reorganise the actomyosin cortex during cell polarisation and locomotion. The localisation of different components can be directly or indirectly visualised using fluorescence microscopy, for high-throughput screening commonly in 2D. A quantitative understanding demands segmentation and tracking of whole cells and fluorescence signals associated with the moving cell boundary, for example those associated with actin polymerisation at the cell front of locomoting cells. As regards segmentation, a wide range of methods can be used (threshold based, region growing, active contours or level sets) to obtain closed cell contours, which then are used to sample fluorescence adjacent to the cell edge in a straightforward manner. The most critical step however is cell edge tracking, which links points on contours at time t to corresponding points at t+1. Optical flow methods have been employed, but usually fail to meet the requirement that total fluorescence must not change. QuimP uses a method (ECMM, electrostatic contour migration method (Tyson et al., 2010) which has been shown to outperform traditional level set methods. ECMM minimises the sum of path lengths connecting all pairs of points, equivalent to minimising the energy required for cell deformation. The original segmentation based on an active contour method and outline tracking algorithms have been described in (Dormann et al., 2002; Tyson et al., 2010; Tyson et al., 2014).

Spot detector detects and counts spots, based on wavelet transform.

- Detects spots in noisy images 2D/3D.
- Depending on objective, spots can be nuclei, nucleus or cell
- Versatile input: sequence or batch of file.
- Detects spot in specific band/channel.
- Multi band labeling: automaticaly creates ROIs from one band and count in the same or an other band.
- Filters detection by size.
- Sort detection by ROIs
- Output data in XLS Excel files: number of detection by ROIs, and each detection location and size.
- Outputs withness image with ROIs and detection painted on it.
- Outputs binary detection image.
- Displays detections
- Displays tags

QuantCenter is the framework for 3DHISTECH image analysis applications. with the goal of helping the pathologists to diagnose in an easier way. QuantCenter, is optimized for whole slide quantification. It has a linkable algorithm concept that tries to provide an easy-to-use and logical workflow. The user has different quantification modules that he or she could link one after other to fine-tune or to speed up the analysis.

QuPath is open source software for Quantitative Pathology. QuPath has been developed as a research tool at Queen's University Belfast.

A workflow in Python to measure muscule fibers corresponding to the method used in Keefe, A.C. et al. Muscle stem cells contribute to myofibres in sedentary adult mice. Nat. Commun. 6:7087 doi: 10.1038/ncomms8087 (2015).

Example image:

muscleQNT/15536_2032_0.tif ...

Neurolucida is a powerful tool for creating and analyzing realistic, meaningful, and quantifiable neuron reconstructions from microscope images. Perform detailed morphometric analysis of neurons, such as quantifying 1) the number of dendrites, axons, nodes, synapses, and spines, 2) the length, width, and volume of dendrites and axons, 3) the area and volume of the soma, and 4) the complexity and extension of neurons. See 10.3389/fnins.2012.00049

SLDC is an open-source Python workflow. SLDC stands for Segment Locate Dispatch Classify. This framework aims at facilitating the development of algorithms for detecting objects in multi-gigapixel images. Particularly, it provides algorithm developers with a structure to define problem-dependent components of their processing workflow (i.e. segmentation and classification) in a concise way. Every other concern such as parallelization and large image handling are encapsulated by the framework. It also features a powerful and customizable logging system and some components to apply several workflows one after another on a same image. SLDC can work on local images or interact with Cytomine

Example image:

Toy image data

MorphoLibJ is a library of plugin for ImageJ with functionalities for image processing such as filtering, reconstructing, segmenting, etc... Tools are based on Mathematical morphology with more rigorous mathematical approach than in the standard tools of ImageJ in particular for surface (or perimeter) measurements which are usually based on voxel counting.  

http://imagej.net/MorphoLibJ#Measurements

Among the features:

Morphological operations :  Dilation, Erosion, Opening,  Closing , Top hat (white and black), Morphological gradient (aka Beucher Gradient), Morphological Laplacian, Morphological reconstruction, Maxima/Minima , Extended Maxima/Minima -Watershed (classic or controlled) -Image overlay -Image labelling -Geodesic diameter -Region Adjacency Graph -Granulometry curves, morphological image analysis.

LocAlization Microscopy Analyzer (LAMA) is a software tool that contains several well-established data post processing algorithms for single-molecule localization microscopy (SMLM) data. LAMA has implemented algorithms for cluster analysis, colocalization analysis, localization precision estimation and image registration. LAMA works with a graphical user interface (GUI), and accepts simple input data formats as supported by various single- molecule localization software tools.

Localization-based super-resolution techniques open the door to unprecedented analysis of molecular organization. This task often involves complex image processing adapted to the specific topology and quality of the image to be analyzed. SR-Tesseler is an open-source segmentation software using Voronoï tessellation constructed from the coordinates of localized molecules. It allows precise, robust and automatic quantification of protein organization at different scales, from the cellular level down to clusters of a few fluorescent markers. SR-Tesseler is insensitive to cell shape, molecular organization, background and noise, allowing comparing efficiently different biological conditions in a non-biased manner, and perform quantifications on various proteins and cell types. SR-Tesseler software comes with a very simple and intuitive graphical user interface, providing direct visual feedback of the results and is freely available under GPLv3 license.

Adiposoft is an automated Open Source software for the analysis of adipose tissue cellularity in histological sections.

Example data can be found on the plugin description page in ImageJ wiki (download link). There is also a link to a MATLAB version of the workflow.

This Matlab code demonstrates an edge-based active contour model as an application of the Distance Regularized Level Set Evolution (DRLSE) formulation.

An automated MATLAB tool for segmentation of surface stained cells

DBSCAN (Density-based spatial clustering of applications with noise) performs multi-dimensional clustering based on the local density of points. This plugin is implemented for 2-3 dimensions.

A clear tutorial on how to write a MATLAB script to segment clustered cells.

The full script is downloadable near the bottom of the article. 

CellDetector can detect cells (or other objects) in microscopy images such as histopathology, fluorescence, phase contrast, bright field, etc. It uses a machine learning-based method where a cell model is learned from simple dot annotations on a few images for training and predict on test sets. The installation requires some efforts but the instruction is well explained. Training parameters should be tuned for different datasets, but the default settings could be a good starting point.

Quantification of HER2 immunohistochemistry.

ImmunoMembrane is an ImageJ plugin for assessing HER2 immunohistochemistry, described in [bib]2472[/bib]. It is important to read the URL documentation and original paper to understand how to use the plugin appropriately.

There is web service available. Users can upload image data to process them and get cell membrane to be segmented: Web ImmunoMembrane

Note also that the pixel size is not read automatically from the image, but rather the source image scale should be entered into the dialog box - and the image rescaled accordingly prior to analysis. This scale value is the inverse of the value normally found for pixel width and pixel height under Image -> Properties... (i.e. pixel width & height are given in microns per pixel; the dialog box asks for pixels per micron).

The QuimP software from Bretschneider group is deployed as ImageJ plugin and includes model-based cell segmentation, cell outline tracking and quantification of the spatially resolved speed of protrusions and retractions. The algorithm to calculate morphological dynamics is faster compared to other approaches (e.g. Machacek and Danuser, 2006). The reference paper describes the workflow for these analyses.

Oufti (previously named MicrobeTracker) is a MATLAB application / suite of tools for analysing fluorescent spots inside microbes. MicrobeTracker can identify cell outlines and fluorescent foci, and generate plots and statistics based on positions and intensity (kymographs, histograms etc.) The MATLAB code is easy to modify and extend to add additional plots and statistics: see e.g. Lesterlin et al. (2014).

The Outfi Forum is quite active.

This protocol first extracts the cell nuclei from a given fluorescence channel (full labeling), and grows a contour from each nucleus to extract the cell edge in another fluorescence channel (membrane-labeling).