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 Segmentation Parameters 1) Intensity value of interest (center) 2) Width of intensity interval (variance) 3) Percentage of data vs. smoothing Curvature Speed

University of Utah

Data speed term

Attract level set to range of voxel intensities

D(I) D(I)= 0 Width (Variance) Center (Mean) I (Intensity)

University of Utah

Curvature speed term

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

University of Utah

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

University of Utah

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)

University of Utah

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)

University of Utah

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

University of Utah

3D User Interface Demo

QuickTime™ and a Video decompressor are needed to see this picture.

University of Utah

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 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