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Level-Set Evolution with Region Competition: Automatic 3-D Segmentation of Brain Tumors

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

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Level-Set Evolution with Region Competition: Automatic 3-D Segmentation of Brain Tumors

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  1. Level-Set Evolution with Region Competition:Automatic 3-D Segmentation of Brain Tumors 1Sean Ho, 2Elizabeth Bullitt, and 1;3Guido Gerig 1Department of Computer Science,2Department of Surgery,3Department 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 • Glioblastomas have a ring enhancement that makes segmentation tough

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

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

  5. 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

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

  7. Enhancement => image force

  8. Live demo 0 1 20 300

  9. 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:

  10. Validation • Compared against expert human rater • Validation with 2nd human rater in progress • More tumor datasets on the way

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

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

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

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