Level-Set Evolution with Region Competition: Automatic 3-D Segmentation of Brain Tumors 1 Sean Ho, 2 Elizabeth Bullitt, and 1;3 Guido Gerig 1 Department of Computer Science, 2 Department of Surgery, 3 Department of Psychiatry University of North Carolina, Chapel Hill, NC, USA Supported by NIH-NCI R01 CA67812.

Partially supported by NIH-NCI P01 CA47982.

Tumor segmentation

  Focusing on meningiomas and glioblastomas Glioblastomas have a ring enhancement that makes segmentation tough

Overview of the procedure

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Fuzzy voxel-based segmentation Level-set snake driven by: Region competition 2.

Multiparameter MR image data Smoothness constraints   Can use alone for enhancing tumors Or as part of the tumor/tissue/vasculature segmentation

Multiparameter MR images

  T1GAD-T1 registered difference image T2 available but not used in this work

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Probability map of enhancing tissue   T1GAD-T1 registered difference image Mixture-model histogram fit:  Gaussian for the background  Gamma function for the contrast agent uptake

Region competition snake

  Image force: modulate propagation by signed inside/outside force Smoothness constraint:   Mean curvature flow Gaussian smoothing of the implicit function

Enhancement => image force

Live demo

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Results

  Very challenging segmentation problem, even for expert manual segmentation:  Complex tumor geometry   Complex greylevel appearance Nearby enhancing structures (e.g. vessels, bone) Some examples:

Validation

   Compared against expert human rater Validation with 2 nd human rater in progress More tumor datasets on the way Dataset Tumor020 Tumor022 Tumor025 Volume Overlap 93.2% 89.5% 84.7% Hausdorff (mm) 6.92

13.02

10.73

In (mm) 0.47

0.49

0.83

Out (mm) 1.07

4.13

1.07

Average (mm) 0.59

1.49

0.85

Integrating in the

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Big Picture

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   Modify atlas   with subject specific pathology Probability map of enhancing tissue Region-competition snake  Smoothness constraints EM tissue classification  Using spatial prior   (previous talk): Additional tumor and edema classes Bias field inhomogeneity compensation Result: Combined tumor and tissue segmentation (gm, wm, csf, edema)

The

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Big Picture

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, cont.

 Tumor segmentation registered with segmentation of vasculature :   We also have MRA images Vessel extraction software

Free software downloads

   midag.cs.unc.edu

SNAP (prototype):   3D level-set evolution Preprocessing pipeline and manual editing VALMET (prototype)