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
• Accelerate level sets with graphics processor
• Unify computation and visualization
University of Utah
University of Utah
Level-Set Segmentation
Surface velocity attracts level set to desired feature
% Smoothing
Data-Based Speed
Curvature Speed
Segmentation Parameters
1) Intensity value of interest (center)
2) Width of intensity interval (variance)
3) Percentage of data vs. smoothing
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Data speed term
Attract level set to range of voxel intensities
Width (Variance)
Center (Mean)
D(I)
D(I)= 0
I (Intensity)
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Curvature speed term
Enforce surface smoothness
• Prevent segmentation “leaks”
• Smooth noisy solution
Seed Surface
No Curvature
With Curvature
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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
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General Computation on GPUs
Streaming architecture
Store data in textures
ForEach loop over data elements
• Fragment program is computational kernel
Texture Data
CPU
Vertex & Texture
Coordinates
Vertex
Processor
Rasterizer
Fragment
Processor
Frame/Pixel
Buffer(s)
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GPU-Based Level-Set Solver
Streaming Narrow-Band Method on GPU
• Multi-dimensional virtual memory
• Optimize for GPU computation
– 2D, minimal memory, data-parallel
Virtual Memory Space
Physical Memory Space
Unused Pages
Inside Outside
Active Pages
University of Utah
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)
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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
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3D User Interface Demo
QuickTime™ and a
Video decompressor
are needed to see this picture.
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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
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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”
University of Utah
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
University of Utah