Filament tracing operations are image analysis operations in which there is an image of a filamentous structure (it may be a tree-like structure, a filament network or a agglomeration of single 'stick-like' filaments) as input and outputs data that represent the filament, most commonly a skeleton representation of the filaments and their diameters or surfaces.

## NET - Network Extraction Tool

The ultimate goal of the *NET* framework is to make images of networks processable by computers. Therefore we want to have a pixel based image as input, as output we want a representation of the network visible in the image that retains as much information about the original network as possible. *NET* achives this by first segmenting the image and then vectorizing the network and then extracting information. The information we extract is

- First and foremost the graph of the network. We find the crossings (nodes) and connections between crossings (edges) and therefore extract information about the neighborhood relations, the
*topology*of the network. - We also extract the coordinates of all nodes which enables us to embed them into space. We therefore extract information about the
*geometry*of the network. - Last but not least we track the radii of the edges in the extraction process. Therefore every edge has a radius which can be identified with its conductivity.

In the following we will first provide detailed instructions on how to install *NET* on several platforms. Then we describe the functionality and options of each of the four scripts that make up the *NET* framework.

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## ORION

ORION: Online Reconstruction and functional Imaging Of Neurons: segmentation and tracing of neurons for reconstruction.

A project to develop tools that explore single neuron function via sophisticated image analysis. ORION software bridges advanced optical imaging and compartmental modeling of neuronal function by rapidly, accurately, and robustly generating, from structural image data, a cylindrical morphology model suitable for simulating neuronal function. The goal of this project is to develop a computational and experimental framework to allow real-time mapping of functional imaging data (e.g., spatio-temporal patterns of dendritic voltages or intracellularions) to neuronal structure, during the very limited duration of an acute experiment.

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## NeuronMetrics

The invention comprises a software tool, NeuronMetrics, which functions as a set of modules that run in the open-source program ImageJ. NeuronMetrics features a novel method for estimating neural “branch number” (a measure of the axonal complexity) from two-dimensional images. In addition, the tool features a novel method for modeling neural structure in large “gaps” that result from image artifacts.

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## Neural Circuit Tracer

Neural Circuit Tracer (NCTracer) is open source software for automated and manual tracing of neurites from light microscopy stacks of images. NCTracer has more than one workflow available for neuron tracing.

"The Neural Circuit Tracer is open source software built using Java (Sun Microsystems) and Matlab (MathWorks, Inc., Natick MA). It is based on the core of ImageJ (http://rsbweb.nih.gov/ij) and the graphic user interface has been developed by using Java Swings. The software combines anumber of functionalities of ImageJ with several newly developed functions for automated and manual tracing of neurites. The Neural Circuit Tracer is designed in a way

that will allow the users to add any plug-ins developed for ImageJ. More importantly, functions written in MatLab and converted into Java with Matlab JA toolbox can also be added to the Neural Circuit Tracer."

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## AnaMorf

AnaMorf is a plug-in developed for the ImageJ platform (rsb.info.nih.gov/ij) to analyse the microscopic morphology of filamentous microbes. The program returns average data on a population of mycelial elements, using the descriptors projected area, circularity, total hyphal length, number of hyphal tips, hyphal growth unit, lacunarity and fractal dimension. The plug-in accepts as input a user-specified directory of images, analysing each and outputing tabulated results.

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## AnalyzeSkeleton

This plugin tags all pixel/voxels in a skeleton image and then counts all its junctions, triple and quadruple points and branches, and measures their average and maximum length.

he tags are shown in a new window displaying every tag in a different color. You can find it under [Plugins>Skeleton>Analyze Skeleton (2D/3D)]. See Skeletonize3D for an example of how to produce skeleton images.

The voxels are classified into three different categories depending on their 26 neighbors: - End-point voxels: if they have less than 2 neighbors. - Junction voxels: if they have more than 2 neighbors. - Slab voxels: if they have exactly 2 neighbors.

End-point voxels are displayed in blue, slab voxels in orange and junction voxels in purple.

Notice here that, following this notation, the number of junction voxels can be different from the number of actual junctions since some junction voxels can be neighbors of each other.

Output data type: table result, image of the skeleton

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## hIPNAT

hIPNAT (**h**IPNAT: **I**mage **P**rocessing for **N**euro**A**natomy and **T**ree-like structures) is a set of tools for the analysis of images of neurons and other tree-like morphologies. It is written for ImageJ, the *de facto* standard in scientific image processing. It is available through the ImageJ Neuroanatomy update site.

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## Rivulet

"we propose a novel automatic 3D neuron reconstruction algorithm, named Rivulet, which is based on the multi-stencils fast-marching and iterative back-tracking. The proposed Rivulet algorithm is capable of tracing discontinuous areas without being interrupted by densely distributed noises."

This plugin can be used with default parameters or with user-defined parameters.

Example image obtained from Rivulet Wiki website (https://github.com/RivuletStudio/Rivulet-Neuron-Tracing-Toolbox/wiki)

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## TReMAP

"we present a new fully automated 3D reconstruction algorithm, called TReMAP, short for Tracing, Reverse Mapping and Assembling of 2D Projections. Instead of tracing a 3D image directly in the 3D space as seen in majority of the tracing methods, we first trace the 2D projection trees in 2Dplanes, followed by reverse-mapping the resulting 2D tracing results back into the 3D space as 3D curves; then we use a minimal spanning tree (MST) method to assemble all the 3D curves to generate the final 3D reconstruction. Because we simplify a 3D reconstruction problem into 2D, the computational costs are reduced dramatically."

Suitable for high throughput neuron image analysis (image sizes >10GB). This plugin can be used with default parameters or user-defined parameters.

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## APP2

All-path-pruning 2.0 (APP2) is a component of Vaa3D. APP2 prunes an initial reconstruction tree of a neuron’s morphology using a long-segment-first hierarchical procedure instead of the original termini-first-search process in APP. APP2 computes the distance transform of all image voxels directly for a gray-scale image, without the need to binarize the image before invoking the conventional distance transform. APP2 uses a fast-marching algorithm-based method to compute the initial reconstruction trees without pre-computing a large graph. This method allows to trace large images. This method can be used with default parameters or user-defined parameters.

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