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nctuTW is a "high-throughput computer method of reconstructing the neuronal structure of the fruit fly brain. The design philosophy of the proposed method differs from those of previous methods. We propose first to compute the 2D skeletons of a neuron in each slice of the image stack. The 3D neuronal structure is then constructed from the 2D skeletons. Biologists tend to use confocal microscopes for optimal images in a slice for human visualization; and images in two consecutive slices contain overlapped information. Consequently, a spherical object becomes oval in the image stack; that is, neurons in the image stack do not reflect the true shape of the neuron. This is the main reason we chose not to work directly on the 3D volume.

The proposed method comprises two steps. The first is the image processing step, which involves computing a set of voxels that is a superset of the 3D centerlines of the neuron. The shortest path graph algorithm then computes the centerlines. The proposed method was applied to process more than 16 000 neurons. By using a large amount of reconstructions, this study also demonstrated a result derived from the reconstructed data using the clustering technique." (Extracted from reference publication)

Illustrative image shows gold standard (top) and method results (bottom). 

Quote: "The GDSC ImageJ plugins are a collection of analysis programs for microscopy images including colocalisation analysis and peak finding (FindFoci)."

Many types of analysis besides simply finding foci detection (spot detection) is bundled in this plugin. One prominent function is "FindFoci Optimizer". This allows feeding images with spot annotation by the user (multi-point selection tool) and scans through various parameter combinations to find the best parameter set that gives the results similar to the annotation. This is almost like machine learning... but with well-established parameter types that allows you to fully understand what is going on.

This plugin filters a 3D image stack (or 2D image) to produce a score for how "tube-like" each point in the image is. This is useful as a preprocessing step for tracing neurons or blood vessels, for example. For 3D image stacks, the plugin uses the eigenvalues of the Hessian matrix to calculate this measure of "tubeness", using a metrics mentioned in Sato et al 1997 ¹: if the larger two eigenvalues (λ₂ and λ₃) are both negative then value is √(λ₂λ₃), otherwise the value is 0. For 2D images, if the large eigenvalue is negative, we return its absolute value and otherwise return 0.

This plugin is now bundled as part of Fiji.

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)

JFilament is an ImageJ plugin for segmentation and tracking of 2D and 3D filaments in fluorescenece microscopy images. The main algorithm used in Jfilament is "Stretching Open Active Contours" (SOAC). In order to use this method, the user must define seed points in the image where the SOAC method will begin.

JFilament also includes 2D "closed" active contours which can be used for tasks such as segmentation and tracking of cell boundaries.