Documentation/Nightly/Modules/RobustStatisticsSegmenter

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For the stable Slicer documentation, visit the 4.6 page.


Introduction and Acknowledgements

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 NA-MIC website.
Author: Yi Gao, UAB
Contributor: Ron Kikinis, BWH
Contributor: Marc Niethammer, UNC
Contributor: Sylvain Bouix, BWH
Contributor: Martha Shenton, BWH
Contributor: Allen Tannenbaum, SBU
Contact: Yi Gao,

National Alliance for Medical Image Computing (NA-MIC)  
The University of Alabama at Birmingham  
Surgical Planning Laboratory (SPL)  

Module Description

Active contour segmentation using robust statistic.

This module is a general purpose segmenter. The target object is initialized by a label map. An active contour model then evolves to extract the desired boundary of the object.

Use Cases

Most frequently used for these scenarios:

  • Use Case 4, Lung segmentation from CT image:

Tutorials

  • First run:
  1. Give a rough estimate of the object volume and use the editing module to paint several non-zero labels, called seeds in the following, in the object.
  2. Run the module using the default parameters.
  • Note:
  1. The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
  2. The positions of the seeds have to be in the object, preferably close to center.
  • Troubleshooting
    • Surface is too rough. Try:
      • Increase "Boundary smoothness"
    • Leakage into thin/narrow regions. Try:
      • Increase "Boundary smoothness"
    • leakage into similar (but still different) intensity regions (which is not necessarily thin), Try:
      • Increase "Intensity homogeneity"
    • Some regions are missed: Try (either one):
      • Increase "Max volume"
      • Decrease"Intensity homogeneity"
      • Decrease "Boundary smoothness"
    • Some regions are missed, at the same time leakages to some other regions. Try (either one)
      • Increase "Intensity homogeneity"
      • Add some other seeds


Panels and their use

  • Parameters panel:
    • Approximate volume: The estimated upper limit of the target volume. The resulting volume will be less or equal than this value.
    • Intensity homogeneity: If the target contains homogeneous intensity, then give a close-to-1 value here.
    • Boundary smoothness: Larger value will result in smoother boundary and a more spherical looking result.
    • Output Label Value: Defined the label value of the output. Also refer to the "Multiple-value label map handling" above.
    • Max running time: The upper limit for program running time.
  • IO panel:
    • Input Image: The image to be segmented.
    • Label Image: The label map providing initial seeds.
  • Output Volume: The output volumetric image.
User Interface

Similar Modules

References

Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours, Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002

Information for Developers