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Interactive, GPU-Based Level Sets for 3D Segmentation

Interactive, GPU-Based Level Sets for 3D Segmentation. Aaron Lefohn Joshua Cates Ross Whitaker University of Utah. Problem Statement. Goal Interactive and general volume segmentation tool using deformable level-set surfaces Challenges Nonlinear PDE on volume Free parameters Solution

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Interactive, GPU-Based Level Sets for 3D Segmentation

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  1. Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah

  2. Problem Statement • Goal • Interactive and general volume segmentation tool using deformable level-set surfaces • Challenges • Nonlinear PDE on volume • Free parameters • Solution • Accelerate level sets with graphics processor • Unify computation and visualization

  3. Level-Set Segmentation • Surface velocity attracts level set to desired feature • Segmentation Parameters 1) Intensity value of interest (center) 2) Width of intensity interval (variance) 3) Percentage of data vs. smoothing % Smoothing Data-Based Speed Curvature Speed

  4. Data speed term D(I) I (Intensity) D(I)= 0 • Attract level set to range of voxel intensities Width (Variance) Center (Mean)

  5. Curvature speed term • Enforce surface smoothness • Prevent segmentation “leaks” • Smooth noisy solution Seed Surface No Curvature With Curvature

  6. Why GPU-Based Level-Set Solver? • Inexpensive, fast, SIMD co-processor • Cheap (~$400) • Over 10x more computational power than CPU • Fast access to texture memory (2D/3D) • Example GPUs • ATI Radeon 9x00 Series • NVIDIA GeForceFX Series

  7. General Computation on GPUs Texture Data CPU Vertex & Texture Coordinates Frame/Pixel Buffer(s) Vertex Processor Fragment Processor Rasterizer • Streaming architecture • Store data in textures • ForEach loop over data elements • Fragment program is computational kernel

  8. GPU-Based Level-Set Solver Physical Memory Space Unused Pages Inside Outside Active Pages • Streaming Narrow-Band Method on GPU • Multi-dimensional virtual memory • Optimize for GPU computation • 2D, minimal memory, data-parallel Virtual Memory Space

  9. Evaluation User Study • Goal • Can a user quickly find parameter settings to create an accurate, precise 3D segmentation? • Relative to hand contouring • Methodology • Six users and nine data sets • Harvard Brigham and Women’s Hospital Brain Tumor Database • 256 x 256 x 124 MRI • No pre-processing of data & no hidden parameters • Ground truth • Expert hand contouring • STAPLE method (Warfield et al. MICCAI 2002)

  10. Evaluation Results • Efficiency • 6 ± 3 minutes per segmentation (vs multiple hours) • Solver idle 90% - 95% of time • Precision • Intersubject similarity significantly better • Accuracy • Within error bounds of expert hand segmentations • Bias towards smaller segmentations • Compares well with other semi-automatic techniques • Kaus et al. 2001

  11. 3D User Interface Demo

  12. Conclusions • 1. GPU power interactive level-set computation • Streaming narrow-band algorithm • Dynamic, sparse computation model for GPUs • 2. Interactive level-sets powerful segmentation tool • Intuitive, graphical parameter setting • Quantitatively comparable to other methods • Much faster than hand segmentations • No pre-processing of data & no hidden parameters • Future work • Other segmentation classifiers • User interface enhancements • More information on GPU level-set solver • See IEEE TVCG paper, “A Streaming Narrow-Band Algorithm” • Google “Lefohn streaming narrow”

  13. Acknowledgements • Joe Kniss • Gordon Kindlmann • Milan Ikits • SCI faculty, students, and staff • John Owens at UCDavis • ATI Technologies, Inc • Evan Hart, Mark Segal, Arcot Preetham, Jeff Royle, and Jason Mitchell • Brigham and Women’s Hospital Tumor Data • Simon Warfield, Michael Kaus, Ron Kikinis, Peter Black, and Ferenc Jolesz • Funding • National Science Foundation grant #ACI008915 and #CCR0092065 • NIH Insight Project

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