Documentation/4.0/Modules/RobustStatisticsSegmenter

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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, Georgia Tech
Contributor1: Allen Tannenbaum (Author): Georgia Tech
Contributor2: Ron Kikinis, BWH
Contact: Yi Gao,

Georgia Tech  
Surgical Planning Laboratory  

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:

Drawn some seeds in the editor module

Set up in RSS module panel, and run

RSS results for extracting meningioma

    • parameters:
      • Intensity homogeneity: 0.5
      • Boundary smoothness: 0.5
  • Use Case 2, right kidney, CT image:

RSS segments right kidney from CT image

  • Use Case 3, left kidney, CT image:

RSS segments left kidney from CT image

  • Use Case 4, Lung, CT image:
    • Segment lung from CT image
    • Test case file http://pubimage.hcuge.ch:8080/ LUNGIX data set
    • parameters:
      • Intensity homogeneity: 0.7
      • Boundary smoothness: 0.4

RSS segments lung 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

Y. Gao, A. Tannenbaum, R. Kikinis, Simultaneous Multi-Object Segmentation using Local Robust Statistics and Contour Interaction, MICCAI 2010, Medical Computer Vision, Workshop on File:Rss.pdf

Information for Developers

Work in progress
 Section under construction.
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