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.

Synonyms
Tubular structure extraction
biofilament tracing
Curvilinear structure reconstruction
Curvilinear structure detection
neuron image analysis
neuron reconstruction

Paintera

Description

Paintera is a general visualization tool for 3D volumetric data and proof-reading in segmentation/reconstruction with a primary focus on neuron reconstruction from electron micrographs in connectomics. It features/supports:

  •  Views of orthogonal 2D cross-sections of the data at arbitrary angles and zoom levels
  •  Mipmaps for efficient display of arbitrarily large data at arbitrary scale levels
  •  Label data
    •  Painting
    •  Manual agglomeration
    •  3D visualization as polygon meshes
      •  Meshes for each mipmap level
      •  Mesh generation on-the-fly via marching cubes to incorporate painted labels and agglomerations in 3D visualization. Marching Cubes is parallelized over small blocks. Only relevant blocks are considered (huge speed-up for sparse label data).

Paintera is implemented in Java and makes extensive use of the UI framework JavaFX

Paintera screenshot

HyphaTracker

Description
HyphaTrackerWorkflow
HyphaTracker Workflow

HyphaTracker propose a workflow for time-resolved analysis of conidia germination. Each part of this workflow can also be used independnatly , as a toolbox. It has been tested on bright-field microscopic images of conidial germination. Its purpose is mainly to identify the germlings and to remove crossing hyphae, and measure the dynamics of their growth.

hyphatracker

MicrotubuleTracker in FIJI

Description

MTrack is a tool, which detects, tracks, and measures the behavior of fluorescently labeled microtubules imaged by TIRF (total internal reflection fluorescence) microscopy. In such an in vitro reconstitution approach, stabilized, non-dynamic microtubule seeds serve as nucleation points for dynamically growing microtubules.

MTrack is a bi-modular tool. The first module detects and tracks the growing microtubule ends and creates trajectories. The second module uses these trajectories to fit models of dynamic behavior (polymerization and depolymerization velocities, catastrophe and rescue frequencies). It also computes statistics such as length and lifetime distributions when analyzing more than one movie (batch mode).

has topic
Track Filament shaped objects and analyze tracks using Ransac fits.

NeuriteTracer

Description

"The plugin analyzes fluorescence microscopy images of neurites and nuclei of dissociated cultured neurons. Given user-defined thresholds, the plugin counts neuronal nuclei, and traces and measures neurite length."[...]" NeuriteTracer is a fast simple-to-use ImageJ plugin for the analysis of outgrowth in two-dimensional fluorescence microscopy images of neuronal cultures. The plugin performed well on images from three different types of neurons with distinct morphologies."

This plugin requires parameter setting: Threshold levels and scale (see more details on the related publication)

MOST-Raytracer

Description

This project was designed for vectorize and analyze the  blood vessels in the mouse brain.

This plugin requires the definition of seed point detection settings by the user (Semi-automated).

has topic
need a thumbnail

Simple Tracing DF-Tracing

Description

We have developed a novel approach, named DF-Tracing, to tackle this challenge. This method first extracts the neurite signal (foreground) from a noisy image by using anisotropic filtering and automated thresholding. Then, DF-Tracing executes a coupled distance-field (DF) algorithm on the extracted foreground neurite signal and reconstructs the neuron morphology automatically. Two distance-transform based “force” fields are used: one for “pressure”, which is the distance transform field of foreground pixels (voxels) to the background, and another for “thrust”, which is the distance transform field of the foreground pixels to an automatically determined seed point. The coupling of these two force fields can“push” a “rolling ball” quickly along the skeleton of a neuron, reconstructing the 3D cell morphology.

Simple Tracing - DT-fields

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

JFilament

Description

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.

 

JFilament_ImageJ_pulgin_Window