1. 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.

2. Tumor segmentation Focusing on meningiomas and glioblastomas Focusing on meningiomas and glioblastomas Glioblastomas have a ring enhancement that makes segmentation tough Glioblastomas have a ring enhancement that makes segmentation tough

3. Overview of the procedure 1. Multiparameter MR image data 2. Fuzzy voxel-based segmentation 3. Level-set snake driven by: 1. Region competition 2. Smoothness constraints Can use alone for enhancing tumors Can use alone for enhancing tumors Or as part of the tumor/tissue/vasculature segmentation Or as part of the tumor/tissue/vasculature segmentation

4. Multiparameter MR images T1GAD-T1 registered difference image T1GAD-T1 registered difference image T2 available but not used in this work T2 available but not used in this work = -

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

6. Region competition snake Image force: modulate propagation by signed inside/outside force Image force: modulate propagation by signed inside/outside force Smoothness constraint: Smoothness constraint: Mean curvature flow Mean curvature flow Gaussian smoothing of the implicit function Gaussian smoothing of the implicit function

7. Enhancement => image force

8. Live demo 0 20300 1

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

10. Validation Compared against expert human rater Compared against expert human rater Validation with 2 nd human rater in progress Validation with 2 nd human rater in progress More tumor datasets on the way More tumor datasets on the way Dataset Volume Overlap Hausdorff (mm) In (mm) Out (mm) Average (mm) Tumor02093.2%6.920.471.070.59 Tumor02289.5%13.020.494.131.49 Tumor02584.7%10.730.831.070.85

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

12. The “ Big Picture ”, cont. Tumor segmentation registered with segmentation of vasculature: Tumor segmentation registered with segmentation of vasculature: We also have MRA images We also have MRA images Vessel extraction software Vessel extraction software

13. Free software downloads midag.cs.unc.edu midag.cs.unc.edu SNAP (prototype): SNAP (prototype): 3D level-set evolution 3D level-set evolution Preprocessing pipeline and manual editing Preprocessing pipeline and manual editing VALMET (prototype) VALMET (prototype)