Difference between revisions of "Documentation/4.0/Modules/EMSegment Easy"

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This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].<br>
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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 [http://nac.spl.harvard.edu/pages/Overview NAC website].<br>
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Author: FIRSTNAME LASTNAME, AFFILIATION<br>
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Author: Kilian Pohl, UPenn <br>
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Contributor1: Daniel Haehn, UPENN<br>
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Contact: Kilian Pohl, <email>pohl.kilian@gmail.com</email><br>
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|Image:UPenn-logo.png|University of Pennsylvania
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|Image:Logo-splnew.jpg|Surgical Planning Laboratory   <-Replace this logo with yours
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|Image:Logo-splnew.jpg|Surgical Planning Laboratory  
 
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This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].<br>
 
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This module is designed for users who want to do a quick intensity based image segmentation. The remainder of this sections describes the work flow of the two modes in further detail:
  
 
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Most frequently used for these scenarios:
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MR image segmentation
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* Use Case 1:
 
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* Use Case 2:
 
  
 
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N/A
  
 
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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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=== Step 1: Define Input Datasets  ===
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Define the volumes to be segmented. Each volume has to be from the subject and has to represent a different mode, such as T1 and FLAIR, than the other input volumes.
 +
 
 +
=== Step 2: Define Structure  ===
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The user specifies the number of structures to be segmented and the label of each structure. You can further add class to an existing structure by right-clicking on the structure and selecting "Add sub-class". The label and corresponding color of a structure are modified by selecting the structure in the tree.
 +
 
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=== Step 3: Sampling and Class Weights ===
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Define the intensity distribution for each structure of interest by taking samples in the image of interest. Users can also specify the relative to weight of a node in the tree with respect to other structures. If a class should be overestimated in the final label map then this can be adjusted by lowering the weight of that structure and repeating the segmentation.
  
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=== Step 4: Edit Node-based Parameters ===
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Users specify the relative to weight of a node in the tree with respect to other structures which are children of the same parent node. The first tab also specifies the weight of the input channels as well as the atlases. The value 'Alpha' specifies the smoothing applied to the structure (via MRFs). The second tab (Stopping Condition) lists the number of iterations associated with the segmentation task. By default, the Bias iteration is set to -1 which means that it is performed each iteration. If the value is greater -1 then the inhomogeneity computation is stopped after n iterations. The third tab specifies printing out intermediate results, which are saved in the working directory specified in the next step
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* Input panel1:
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=== Step 5: Miscellaneous ===
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** First input
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This panels allows the user to specify the region of interest to be segmented. Pressing the 'Segment' button starts the segmentation.
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** Second input
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* Parameters panel:
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Once the parameters are specified, the target images are segmented
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** First parameter
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using the EM Segmentation algorithm (Pohl et al. TMI 2007). The label map with corresponding statistics is returned after successful completion of the algorithm.
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** Second parameter
 
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* Output panel:
 
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** First output
 
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** Second output
 
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[[Image:screenshotBlankNotOptional.png|thumb|280px|Name of panel 1]]
 
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* Input panel2:
 
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** First input
 
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** Second input
 
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* Parameters panel:
 
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** First parameter
 
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** Second parameter
 
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* Output panel:
 
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** First output
 
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** Second output
 
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[[Image:screenshotBlankNotOptional.png|thumb|280px|Name of panel 2]]
 
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}
 
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* Point to other modules that have similar functionality
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Please visit the [[ Documentation/4.0#Segmentation | segmentation section ]] for similar modules  
  
 
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{{documentation/{{documentation/version}}/module-section|References}}
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Publications related to this module go here. Links to pdfs would be useful.
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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. [[http://people.csail.mit.edu/pohl/publications/journal-citation-bib.html#pohl07_3 bib]]
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For extensions: link to the source code repository and additional documentation
 
  
 
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<pre>{{documentation/{{documentation/version}}/module-cli-xmlurl|{{documentation/modulename}}|SVNREVISION}}</pre>
 
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Using this later template, the final syntax would be:
 
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Latest revision as of 07:33, 14 June 2013

Home < Documentation < 4.0 < Modules < EMSegment Easy


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

This module is designed for users who want to do a quick intensity based image segmentation. The remainder of this sections describes the work flow of the two modes in further detail:

Use Cases

MR image segmentation

Tutorials

N/A

Panels and their use

Step 1: Define Input Datasets

Define the volumes to be segmented. Each volume has to be from the subject and has to represent a different mode, such as T1 and FLAIR, than the other input volumes.

Step 2: Define Structure

The user specifies the number of structures to be segmented and the label of each structure. You can further add class to an existing structure by right-clicking on the structure and selecting "Add sub-class". The label and corresponding color of a structure are modified by selecting the structure in the tree.

Step 3: Sampling and Class Weights

Define the intensity distribution for each structure of interest by taking samples in the image of interest. Users can also specify the relative to weight of a node in the tree with respect to other structures. If a class should be overestimated in the final label map then this can be adjusted by lowering the weight of that structure and repeating the segmentation.

Step 4: Edit Node-based Parameters

Users specify the relative to weight of a node in the tree with respect to other structures which are children of the same parent node. The first tab also specifies the weight of the input channels as well as the atlases. The value 'Alpha' specifies the smoothing applied to the structure (via MRFs). The second tab (Stopping Condition) lists the number of iterations associated with the segmentation task. By default, the Bias iteration is set to -1 which means that it is performed each iteration. If the value is greater -1 then the inhomogeneity computation is stopped after n iterations. The third tab specifies printing out intermediate results, which are saved in the working directory specified in the next step

Step 5: Miscellaneous

This panels allows the user to specify the region of interest to be segmented. Pressing the 'Segment' button starts the segmentation.

Once the parameters are specified, the target images are segmented using the EM Segmentation algorithm (Pohl et al. TMI 2007). The label map with corresponding statistics is returned after successful completion of the algorithm.

Similar Modules

Please visit the segmentation section for similar modules

References

Information for Developers

Work in progress
 Section under construction.


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