Difference between revisions of "Modules:ROISeeding-Documentation-3.6"

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** Minimum Length:  
** Minimum Length:  
** Maximum Length:  
** Maximum Length:  
* '''Viewing panel:'''
* '''Label Definition:'''
=== Caveats and other limitations ===
=== Caveats and other limitations ===

Revision as of 01:56, 20 May 2010

Home < Modules:ROISeeding-Documentation-3.6

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Gallery of New Features

Module Name

Labelmap Seeding

Caption 1

General Information

Module Type & Category

Type: CLI

Category: Diffusion MRI Applications

Authors, Collaborators & Contact

  • Author1: Raúl San José Estépar, BWH
  • Contributor1: Alex Yarmakovich, Isomics
  • Contact: [1]

Module Description

Labelmap Seeding is a tractography implementation that allows a user to seed tracts from a region of interest (ROI). The ROI is defined as a labelmap and has to be provided by the user. One approach to generate the ROI is by means of the Editor module [2]


Examples, Use Cases & Tutorials

  • We want to study the white matter integrate across a population for a given tract. ROISeeding can be used to carry this study.
    • First, a region of interest in the tract that is under study has to be defined for each subject, for example by manually delineating the ROI using the Editor module. The Fractional Anisotropy (FA) Volume can be used as reference volume to trace the ROI. The FA volume can be computed using Diffusion Tensor Scalar Measurements.
    • Second, if the DTI volume is not direcly available, the DWI has to be loaded and the tensor has to be created using the | Diffusion Tensor Estimation.
    • Third, set up the ROI Seeding using the DTI volume and the ROI labelmap as main volume inputs.
    • Fourth, choose the tractography parameters. The stopping criteria are the more important parameters. Depending on the setting different results can be obtained, for example, fiber can be longer or shorter.

Quick Tour of Features and Use

The module is very straighforward to use:

  • Input / Output:
    • Input DTI Volume: set the input DTI volume that is going to be used for tractography.
    • Input Label Map: set the labelmap volume that is going to be used for seeding.
    • Output Fiber bundle: set the resulting fiber bundle that is going to store the result.
    • Write Fibers to Disk: option to save the results to disk for further analysis.
    • Output directory: path where the results are saved.
    • File Prefix Name: name that would be used for each output bundle.
  • Seed Placement Options
    • Seed Spacing: spacing in between seed points within in the labelmap (in mm).
    • Random grid: use random placement.
    • Linear Measure Start Threshold: place seeds only if linear measure (cl) is above this threshold.
  • Tractography Seeding Parameters:
    • Minimum Length:
    • Maximum Length:
  • Label Definition:

Caveats and other limitations

ROISeeding module does not work when the labelmap dimensions do not match the DTI volume dimensions. The work-around is to resample labelmap using | Filtering -> Resample Volume 2.




Known bugs

Follow this link to the Slicer3 bug tracker.

Usability issues

Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.

Source code & documentation

Information about the DTMRI infrastructure in Slicer 3 and related classess can be found here

Links to documentation generated by doxygen.

More Information


Include funding and other support here.


Publications related to this module go here. Links to pdfs would be useful.