RobustStatisticsSegmentation

Robust Statistics Based Segmentation

Usage

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• 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 fiducial points can be thrown into the object. What I do is I just add two fiducial points and move them into the object within one slice.

Testing

Several tests are conducted and shown here along with the parameters used to get the results.

Testing case: left kidney

• Data set: http://wiki.na-mic.org/Wiki/images/8/8d/Patient1.tar.gz
• Approximate volume: 200 mL
• Boundary smoothness: 0.5
• Intensity homogeneity: 0.1

Testing case: tumor

• Data set: http://wiki.na-mic.org/Wiki/images/0/0f/MayExperiments.zip (DiffusionEditorBaselineNode.nrrd)
• Approximate volume: 30 mL
• Intensity homogeneity: 0.1

Testing case: tumor

• Data set: http://www.spl.harvard.edu/publications/bitstream/download/4217 (case3/grayscale.nrrd)
• Approximate volume: 30 mL
• Intensity homogeneity: 0.4

Testing case: tumor

• Data set: http://www.spl.harvard.edu/publications/bitstream/download/4217 (case5/grayscale.nrrd)
• Approximate volume: 3 mL
• Intensity homogeneity: 0.5
• Boundary smoothness: 0.4

This is a difficult case. All the other methods tested either captures only the middle dark spot, or leaks out of the tumor. But the method here nicely capture both the dark core and the bright shell, without leaking out to regions with intensities in the middle.

Key Investigators

Georgia Tech: Yi Gao and Allen Tannenbaum

BWH: Katie Hayes, Andriy Fedorov, and Ron Kikinis