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The primary function of the <B>EMSegmenter</B> module is to step the user through the process of calibrating, via algorithm parameters, the segmentation algorithm to a particular set of input data. However, once a successful collection of parameters is established, the user will commonly want to bypass this detailed calibration process when segmenting new images by using those parameters collected from the calibration process in <B>EMSegmenter</B> module. The <B>EMSegment Command-line module</B> which wraps the <B>EMSegmentCommandLine executable</B> provides this batch processing capability. The <B>EMSegment Command-line module</B> is automatic generated from the <B>EMSegmentCommandLine executable</B> using the --xml option.
 
 
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This module is especially appropriate for segmenting large quantity of data by scripted/batch processing. It requires predefined parameter set, and new target/atlas images can be specified on command line. A list of predefined task is given [[ EMSegmenter-Tasks | here]].
  
 
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Most frequently used for these scenarios:
 
  
* Use Case 1:
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These slides and data describe the Slicer3 implementation of all three EMSegment modules and demonstrate their use.
* Use Case 2:
 
  
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[[Media:EMSegTutorial-AHM2008.ppt | Slides]] - [[Media:EMSegTutorial-AHM2008.zip | Data]]
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A list of all the panels in the interface, their features, what they mean, and how to use them. For instance:
 
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Pohl K, Bouix S, Nakamura M, Rohlfing T, McCarley R, Kikinis R, Grimson W, Shenton M, Wells W. [http://www.slicer.org/pages/Special:PubDB_View?dspaceid=608 A Hierarchical Algorithm for MR Brain Image Parcellation.] IEEE Transactions on Medical Imaging. 2007 Sept;26(9):1201-1212.
For extensions: link to the source code repository and additional documentation
 
  
 
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If your module is available in Slicer repository, the following template could be useful to obtain the corresponding URL:
 
<pre>{{documentation/{{documentation/version}}/module-cli-xmlurl|{{documentation/modulename}}|SVNREVISION}}</pre>
 
 
 
Using this later template, the final syntax would be:
 
<pre>{{documentation/{{documentation/version}}/module-category|xmlurl={{documentation/{{documentation/version}}/module-cli-xmlurl|{{documentation/modulename}}|SVNREVISION}} }}</pre>
 
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Latest revision as of 07:31, 14 June 2013

Home < Documentation < 4.0 < Modules < EMSegment Command-line


For the latest Slicer documentation, visit the read-the-docs.



Introduction and Acknowledgements

This work was funded by the ARRA Supplement to the Neuroimage Analysis Center (NAC), funded by the National Institutes of Health. Information on NAC can be obtained from the NAC website.
Author: Kilian Pohl, UPenn
Contributor1: Daniel Haehn, UPENN
Contact: Kilian Pohl, <email>pohl.kilian@gmail.com</email>

University of Pennsylvania  
Surgical Planning Laboratory  

Module Description

Use Cases

The primary function of the EMSegmenter module is to step the user through the process of calibrating, via algorithm parameters, the segmentation algorithm to a particular set of input data. However, once a successful collection of parameters is established, the user will commonly want to bypass this detailed calibration process when segmenting new images by using those parameters collected from the calibration process in EMSegmenter module. The EMSegment Command-line module which wraps the EMSegmentCommandLine executable provides this batch processing capability. The EMSegment Command-line module is automatic generated from the EMSegmentCommandLine executable using the --xml option.

This module is especially appropriate for segmenting large quantity of data by scripted/batch processing. It requires predefined parameter set, and new target/atlas images can be specified on command line. A list of predefined task is given here.

Tutorials

These slides and data describe the Slicer3 implementation of all three EMSegment modules and demonstrate their use.

Slides - Data

Panels and their use

A list of all the panels in the interface, their features, what they mean, and how to use them. For instance:

Parameters:





     * '
     
       ** ': 
     
   
 


References

Pohl K, Bouix S, Nakamura M, Rohlfing T, McCarley R, Kikinis R, Grimson W, Shenton M, Wells W. A Hierarchical Algorithm for MR Brain Image Parcellation. IEEE Transactions on Medical Imaging. 2007 Sept;26(9):1201-1212.

Information for Developers