Aaron Lefohn
Neoptica / Intel
Interactive Level-Set Segmentation
Interactive Level-Set Segmentation
on the GPU
Problem Statement
Goal
Interactive system for deformable surface manipulation Level-sets
Challenges
Deformation is slow
Deformation is hard to control
Solution
Collaborators
University of Utah
Joe Kniss
Joshua Cates
Charles Hansen
Ross Whitaker
Deformable Surfaces
Applications of Level-Sets
Fluid simulation
Surface reconstruction for 3D scanning Surface processing
Image / Volume segmentation
Introduction Introduction
Level-Set Method
Implicit surface
Distance transform
denotes inside/outside
Surface motion
Introduction IntroductionCPU Level-Set Acceleration
Initialize Domain Compute Update DomainNarrow-Band/Sparse-Grid
Compute PDE only near the surface
Adalsteinson et al. 1995 Whitaker et al. 1998
Peng et al. 1999
Houston et al., 2004
Museth et al., 2004 - 2006
Time-dependent, sparse-grid solver
Introduction Introduction
GPU Level-Set History
Strzodka et al. 2001
2D level-set solver on NVIDIA GeForce 2 No narrow-band optimization
Lefohn et al. 2002
Brute force 3D implementation on ATI Radeon 8500 No faster than CPU, but ~10x more computations No narrow-band optimization
Lefohn et al. 2003 / 2004
Narrow band GPU 3D level set solver
Crane et al. 2007
3D level set solver as part of fluid simulation in NVIDIA G80 launch demo Mask unused grid cells
Kolb et al. 2007
GPU particle level sets
Introduction Introduction
GPU Level-Set History
Strzodka et al. 2001
2D level-set solver on NVIDIA GeForce 2 No narrow-band optimization
Lefohn et al. 2002
Brute force 3D implementation on ATI Radeon 8500 No faster than CPU, but ~10x more computations No narrow-band optimization
Lefohn et al. 2003 / 2004
Narrow band GPU 3D level set solver
Crane et al. 2007
3D level set solver as part of fluid simulation in NVIDIA G80 launch demo Mask unused grid cells
Kolb et al. 2007
GPU particle level sets
Introduction Introduction
Sparse Volume Computation
CPU algorithm: Traverse list of active voxels
GPU algorithm: Compute all active voxels in parallel
Data structures change after each PDE time step
GPU Narrow-Band Solver
Initialize Domain Compute Update Domain Algorithm Algorithm
A Dynamic, Sparse GPU Solver
GPU: Computes PDE
CPU: Manages GPU memory
Algorithm
CPU
GPU
Physical Addresses for Active Memory Pages
Memory Requests
PDE Computation 15-250 passes
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)
Data-Based Speed
Curvature Speed
% Smoothing
Data speed term
Attract level set to range of voxel intensities
D(I)= 0 D(I)
I (Intensity)
Curvature speed term
Enforce surface smoothness
Prevent segmentation “leaks” Smooth noisy solution
Interactive 3D Level Set Visualization
Use GPU to perform interactive volume rendering of
the level set solution while it evolves
Render with original data
Directly render level set data without reformatting data 3D user interface to guide evolving level set surface
Multi-Dimensional Virtual Memory
3D virtual memory 2D physical memory 16 x 16 pixel pages
Algorithm
Direct Volume Rendering of Level Set
Reconstruct 2D Slice of Virtual Memory Space
On-the-fly decompression on GPU
Use 2D geometry and texture coordinates
Direct Volume Rendering of Level Set
Deferred Filtering: Volume Rendering Compressed Data
2D slice-based rendering: No data duplication Tri-linear interpolation
Full transfer function and lighting capabilities
Level-Set Segmentation Application
Idea: Segment Surface from 3D Image
Begin with “seed” surface
Deform surface into target segmentation
Demo
Segmentation of MRI volumes
1283 scalar volume
Hardware Details
ATI Radeon 9800 Pro 1.7 GHz Intel Pentium 4 1 GB of RAM
Region-of-Interest Volume Rendering
Limit extent of volume rendering
Use level-set segmentation to specify region Add level-set value to transfer function
Evaluation User Study
Goal
Can a user quickly find parameter settings to create an accurate, precise 3D segmentation?
Relative to hand contouring
User Study 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)
User Study Results
Efficiency
6 ± 3 minutes per segmentation (vs multiple hours) Solver idle 90% - 95% of time
Precision
Intersubject similarity significantly better 94.04% ± 0.04% vs. 82.65% ± 0.07%
Accuracy
Within error bounds of expert hand segmentations Compares well with other semi-automatic techniques
Kaus et al., Radiology, 2001
Summary
Interactive Level-Set System
10x – 15x speedup over optimized CPU implementation Intuitive parameter tuning
User study evaluation
But…
That was three+ years ago…
GPUs are 6-7x faster!
New GPU capabilities make building dynamic data
structures easier and more efficient
GPU data structures better understood (Glift, etc.)
New, faster CPU level-set methods (RLE, etc.)
Future Directions
Other Level-Set Applications
Surface processing, surface reconstruction, physical simulation
Better User Interface for Level Sets
Add more user control of evolving level set solver More powerful editing of level set solution
“Interactive Visulation”
User-controllable PDE solvers
Combine automatic and by-hand methods
Acknowledgements
Joe Kniss – Volume rendering Josh Cates – Tumor user study
Gordon Kindlmann – “Teem” raster-data toolkit
Milan Ikits – “GLEW” OpenGL extension wrangler Ross Whitaker, Charles Hansen, Steven Parker and John Owens ATI: Evan Hart, Mark Segal, Jeff Royle, and Jason Mitchell
Brigham and Women’s Hospital
National Science Foundation Graduate Fellowship Office of Naval Research grant #N000140110033
Questions?
For More Information
Google “Lefohn level set”
http://graphics.cs.ucdavis.edu/~lefohn/
Journal Papers Based on this Work
Lefohn, Kniss, Hansen, Whitaker, “A Streaming Narrow Band
Algorithm: Interactive Computation and Visualization of
Level Sets,” IEEE Transactions on Visualization and Computer
Graphics, 10 (40), Jul / Aug, pp. 422-433, 2004
Cates, Lefohn, Whitaker, “GIST: An Interactive, GPU-Based
