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WASH, Exo84, and cortactin spot detection and codistribution analysis To detect endosomes, an automatic Otsu threshold is applied to the Gaussian-filtered MT1-MMP–positive endosome image (= 1.5 pixels for the sample image). Statistics about each endosome are then saved, for example random positioning of spots can be compared to actual positioning. For each endosome, WASH and Exo84 (or WASH and cortactin) spots are searched for in a neighboring of x pixels in their respective channel.

This macro is meant to segment the cells of a multicellular tissue. It is written for images showing highly contrasted and uniformly stained cell membranes. The geometry of the cells and their organization is automatically extracted and exported to an ImageJ results table. This includes: Cell area, major, minor fitted ellipse radii + major axis orientation and number of neighbors of the cells. Manual correction of the automatic segmentation is supported (merge split cells, split merged cells).

Sample image data is available in the documentation page. 

This macro was designed to measure the size of the scratch wound in a wound scratch assay. It uses an edge-detection and thresholding technique.

An ActionBar is a simple annotated text document that has snippets of Imagej macros or Beanshell arranged into buttons. This tool is very useful when creating custom work flows integrating multiple components. Each component can be linked to a button for a more streamlined and accessible workflow.

The workflow contains a Matlab package (plusTipTracker) for segmentation and tracking of microtubule tips, based on fluorescence time-lapse movies from microtubule tip markers such as EB-GFP. The tracking model accounts for the specific movement characteristics of microtubules Moreover, scripts for secondary analysis of detected microtubule paths are provided.

plusTipTracker is part of u-track 2.0 package. The workflow is described in the reference. 

## Features >The IJBlob library indentifying connected components in binary images. The algorithm used for connected component labeling is: >Chang, F. (2004). A linear-time component-labeling algorithm using contour tracing technique. Computer Vision and Image Understanding, 93(2), 206–220. doi:10.1016/j.cviu.2003.09.002 ##Reference Wagner, T and Lipinski, H 2013. IJBlob: An ImageJ Library for Connected Component Analysis and Shape Analysis. Journal of Open Research Software 1(1):e6, DOI:

A workflow combining ImageJ macro and manually using Trainable Weka Segmentation plugin for counting clumped cells.

An ImageJ macro for calculating empty surfaces on histological slices (ex: tubules in a kidney).

Particle detection is based on "Analyze Particles" in ImageJ. It probably could also be used in spot detection, not limited to centromere. >This macro is described in Bodor et al. (2012). The macro recognizes centromere or kinetochore foci in Delta Vision or TIFF images and determines their centroid position. Fluorescent intensities are then measured for each centromere by placing a small box around the centroid position of the centromere. The peak intensity value within the box is corrected for local background by subtraction of the minimum pixel value.

A menu item in ImageJ that allows you to inspect a 3D stack with orthogonal views (XY, XZ, YZ planes). Slicing plane could be interactively moved by dragging crosses. Pedro Almada wrote a plugin to save the current orthogonal views as montage. ## ImageJ Macro Usages [Save orthogonal views | OrthoSaver](http://uic.igc.gulbenkian.pt/macros/OrthoSaver.ijm) >ImageJ's orthogonal viewer for 3D stacks doesn't let you easily save the current orthogonal view.

ITK-SNAP is a software application used to segment structures in 3D medical images. It can also be used as a 3D annotation tool for deep learning. It is based on ITK, VTK libraries.