Semi-automated

Morphological Segmentation is an ImageJ/Fiji plugin that combines morphological operations, such as extended minima and morphological gradient, with watershed flooding algorithms to segment grayscale images of any type (8, 16 and 32-bit) in 2D and 3D. Morphological Segmentation runs on any open grayscale image, single 2D image or (3D) stack. If no image is open when calling the plugin, an Open dialog will pop up. The user can pan, zoom in and out, or scroll between slices (if the input image is a stack) in the main canvas as if it were any other ImageJ window. On the left side of the canvas there are three panels of parameters, one for the input image, one with the watershed parameters and one for the output options. All buttons, checkboxes and input panels contain a short explanation of their functionality that is displayed when the cursor lingers over them. Image pre-processing: some pre-processing is included in the plugin to facilitate the segmentation task. However, other pre-preprocessing may be required depending on the input image. It is up to the user to decide what filtering may be most appropriate upstream.

Tracking of focal adhesions includes a number of challenges:

1. Detection of focal adhesion regions in areas of highly variable background
2. Separation of "clumped" adhesions in different objects.
3. Dynamics: Focal adhesions dynamically, grow, shrink, change their shape, they can fuse with neighboring adhesions or one adhesion can be split into multiple children.

Würflinger et al (2011) describe how to detect focal adhesion objects and how to track them over time. Interestingly, tracking results are fed back to segmentation to improve separation of clumped adhesions.

The authors implemented the workflow in Matlab, but do not provide a ready-to-use script.

In the commercial image analysis software "Volocity", automated measurement protocols can be constructed by dragging, dropping and configuring a sequence of individual "tasks".

By combining the "Find Objects" task with a subsequent "Track" task, 3D objects can be identified and followed over time. The initial "Find Objects" segmentation can be refined, e.g. using "Separate Touching Objects"; and tracking results in the form of "Measurement Items" can be viewed in tabular form, as a graph, etc.

A commercial image analysis software. It's interface allows to easily perform measurements and image analysis. Your actions can be recorded and a macro (in a basic script language) can then be created. Almost no knowledge in programming is needed. You can also use python. A SDK is also available to develop stand alone applications in c++. Additional modules allow to use specific operations (3D operators... Examples of available categories of operators : filtering, edge detection, mathematical morphology, segmentation, Frequency operations, mathematical/logical operations, measurements...

The quantification is explained in detail in chapter 8 "Cell Polarity - Focal Adhesion and Actin Dynamics in Migrating Cells" in "Bioimage Data Analysis Book" downloadable from here.

For codes and sample images, download the zipped archive (linked under "Download").