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

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===Authors, Collaborators & Contact===
===Authors, Collaborators & Contact===
* Author1: Mark Scully, The Mind Research Network
* Author1: Mark Scully, The University of Iowa
* Author2: H. J. Bockholt, The Mind Research Network
* Author2: H. J. Bockholt, The Mind Research Network
* Contributor1:  
* Contributor1:  
* Contact: Mark Scully, mscully[at]mrn[dot]org
* Contact: Mark Scully, mark-scully[at]uiowa[dot]edu
===Module Description===
===Module Description===

Latest revision as of 19:24, 10 November 2011

Home < Modules:LesionSegmentationApplications-Documentation-3.6

Return to Slicer 3.6 Documentation

Gallery of New Features

Lupus Lesion Segmentation Applications

Lesion Segmentation Method Overview
Segmented Lesion Volume in Brain

General Information

Module Type & Category

Type: CLI

Category: Segmentation

Authors, Collaborators & Contact

  • Author1: Mark Scully, The University of Iowa
  • Author2: H. J. Bockholt, The Mind Research Network
  • Contributor1:
  • Contact: Mark Scully, mark-scully[at]uiowa[dot]edu

Module Description

The lupus lesion segmentation applications are a group of tools for segmenting lesions in lupus patients using the T1-weighted, T2-weighted, and FLAIR images. This cross-platform tool can be run within Slicer3 as an external module, or directly as a command line.

Please see the DBP description for more information.


Use Cases, Examples

This module is specifically for segmenting lesions in lupus patients.


Quick Tour of Features and Use

  • Input: set input images representing the different modalities of the same subject
    • Input T1 Volume: set input T1-weighted image, if available
    • Input T2 Volume: set input T2-weighted image, if available
    • Input FLAIR Volume: set input FLAIR image, if available
    • Input Brain Mask Volume: set input Brain Mask image, if available
    • Lesion segmentation Model File: set to the model file available with the tutorial
  • Output: set output image
    • Output Lesion Mask Volume: The volume that is the result of the segmentation process
User Interface


Notes from the Developer(s)

Algorithms used, library classes depended upon, use cases, etc.


  • Slicer3 modules:


On the Dashboard, these tests verify that the module is working on various platforms:

Known bugs

Links to known bugs in 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

  • Source code
  • Download release source code via svn:
    • cvs -d :pserver:anonymous@www.nitrc.org:/cvsroot/lupuslesion login
    • cvs -d :pserver:anonymous@www.nitrc.org:/cvsroot/lupuslesion checkout lupuslesion

More Information


This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics.


Scully M, Anderson B, Lane T, Gasparovic C, Magnotta V, Sibbitt W, Roldan C, Kikinis R and Bockholt HJ (2010) An automated method for segmenting white matter lesions through multi-level morphometric feature classification with application to lupus. Front. Hum. Neurosci. doi:10.3389/fnhum.2010.00027 http://frontiersin.org/neuroscience/humanneuroscience/paper/10.3389/fnhum.2010.00027/