Nessys

Description

Nessys: Nuclear Envelope Segmentation System

 

Nessys is a software written in Java for the automated identification of cell nuclei in biological images (3D + time). It is designed to perform well in complex samples, i.e when cells are particularly crowded and heterogeneous such as in embryos or in 3D cell cultures. Nessys is also fast and will work on large images which do not fit in memory.


Nessys also offers an interactive user interface for the curation and validation of segmentation results. Think of this as a 3D painter / editor. This editor can also be used to generate manually segmented images to use as ground truth for testing the accuracy of the automated segmentation method.


Finally Nessys, contains a utility for assessing the accuracy of the automated segmentation method. It works by comparing the result of the automated method to a manually generated ground truth. This utility will provide two types of output: a table with a number of metrics about the accuracy and an image representing a map of the mismatch between the result of the automated method and the ground truth.

has function

FluoRender

Description

FluoRender is an interactive rendering tool for confocal microscopy data visualization. It combines the rendering of multi-channel volume data and polygon mesh data, where the properties of each dataset can be adjusted independently and quickly. The tool is designed especially for neurobiologists, allowing them to better visualize confocal data from fluorescently-stained brains, but it is also useful for other biological samples.

FluoRenderer

3-D Density Kernel Estimation

Description

3-D density kernel estimation (DKE-3-D) method, utilises an ensemble of random decision trees for counting objects in 3D images. DKE-3-D avoids the problem of discrete object identification and segmentation, common to many existing 3-D counting techniques, and outperforms other methods when quantification of densely packed and heterogeneous objects is desired. 

Trakem2 lens distortion correction

Description

Calculates and corrects for lens-distortion models including chromatic abberation from confocal stacks.

nctuTW

Description

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: https://doi.org/10.1371/journal.pcbi.1002658)

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

nctuTW_results_example

Reconstruct

Description

By combining multiple image alignment and tracing into one program, Reconstruct (TM) allows images to be processed more efficiently. Tracing can be done directly on the transformed images and alignments can be asily modified. Reconstruct (TM) was developed from years of experience working with high magnification serial section images of brain tissue. (Extracted from User Manual)

"The original platform of the Reconstruct program allows a user to trace objects in serial sections by manually drawing the outline of each object on each section, which is time-consuming. We modified Reconstruct to enable semi-automatic tracing of axons using a region-growing algorithm called wildfire."

Reconstruct_standaloneapp_example_Results

ORION

Description

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.

ORION_example_result

Neural Circuit Tracer

Description

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." 

Example of output from Neural Circuit Tracer

Rivulet

Description

"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

Traceplot_Rivulet

TReMAP

Description

"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.

Example_TReMAP_Result