Difference between revisions of "Documentation/Nightly/Modules/AirwaySegmentation"

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(Moved to github)
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
 
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Extension: [[Documentation/{{documentation/version}}/Extensions/AirwaySegmentation|AirwaySegmentation]]<br>
 
Acknowledgments:
 
This work is supported by NA-MIC, the Slicer Community and University College of Cork.<br>
 
Author: Pietro Nardelli (University College Cork)<br>
 
Contributor1:<br>
 
Contact: Pietro Nardelli, <email>111126418@umail.ucc.ie</email><br>
 
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AirwaySegmentation is a simple CLI module for airway segmentation starting from chest CT images. This CLI uses a modified version of ITK's itkConnectedThresholdImageFilter to segment all the pixels with an intensity below a threshold. The threshold is automatically identified by the module. The user has to specify three fiducial points: one in the trachea, and two in the main bronchi of the left and right lungs. These fiducials are the starting points for the region growing segmentation. No more than 3 seed points are allowed.
 
 
 
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*Airway Segmentation starting from chest CT datasets
 
 
 
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* <span>'''''Segmentation Parameters'''''</span>: Input parameters for segmentation.
 
** <span>'''Airway Label Value'''</span>: The integer value (0-255) to use for the segmentation results. This will determine the color of the segmentation that will be generated by the algorithm.
 
** <span>'''Seeds'''</span>: Seed points for the algorithm. Three seeds points must be placed. The first one in the trachea, the others in the main bronchi of the left and right lungs.
 
 
 
* <span>'''''IO'''''</span>: Input and Output parameters.
 
** <span>'''Input Volume'''</span>: Input chest CT dataset to be segmented.
 
** <span >'''Output Parameters'''</span>: Output label.
 
 
 
The user interface panel:
 
 
 
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The code is available at [https://github.com/PietroNardelli/Slicer-AirwaySegmentation Github].
 
 
 
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[[Category:Documentation/{{documentation/version}}/Modules/Segmentation]]
 
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Latest revision as of 20:53, 21 March 2022

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