Interactive, GPU-Based Level Sets for 3D Segmentation
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Transcript of Interactive, GPU-Based Level Sets for 3D Segmentation
Interactive, GPU-Based Level Sets for 3D Segmentation
Aaron LefohnJoshua CatesRoss Whitaker
University of Utah
University of UtahUniversity 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 UtahUniversity of Utah
University of UtahUniversity of Utah
Surface velocity attracts level set to desired feature
Segmentation Parameters1) Intensity value of interest (center)2) Width of intensity interval (variance)3) Percentage of data vs. smoothing
Level-Set Segmentation
Data-Based Speed Curvature Speed% Smoothing
University of UtahUniversity of Utah
Data speed term
Attract level set to range of voxel intensities
D(I)= 0
D(I)
I (Intensity)
Width (Variance) Center (Mean)
University of UtahUniversity of Utah
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
Vertex & TextureCoordinates
Vertex Processor Rasterizer Fragment
Processor
Texture Data
Frame/Pixel Buffer(s)
CPU
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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
Active PagesInside Outside
University of UtahUniversity 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 aVideo 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• 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 UtahUniversity 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