interactive, gpu-based level sets for 3d segmentation aaron lefohn joshua cates ross whitaker...

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

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

Interactive, GPU-Based Level Sets for 3D Segmentation

Interactive, GPU-Based Level Sets for 3D Segmentation

Aaron LefohnJoshua CatesRoss Whitaker

University of Utah

Aaron LefohnJoshua CatesRoss Whitaker

University of Utah

Page 2: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

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

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

University of UtahUniversity of Utah

Page 4: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

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

Page 5: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

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)

Page 6: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

University of UtahUniversity of Utah

Curvature speed term

Enforce surface smoothness• Prevent segmentation “leaks”• Smooth noisy solution

Seed Surface No Curvature With Curvature

Page 7: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

University of UtahUniversity 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

Page 8: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

University of UtahUniversity of Utah

General Computation on GPUs

Streaming architecture Store data in textures ForEach loop over data elements

• Fragment program is computational kernel

Vertex & TextureCoordinates

Vertex Processor

RasterizerFragmentProcessor

Texture Data

Frame/Pixel Buffer(s)

CPU

Page 9: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

University of UtahUniversity 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

Active PagesInside Outside

Page 10: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

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)

Page 11: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

University of UtahUniversity 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

Page 12: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

University of UtahUniversity of Utah

3D User Interface Demo

QuickTime™ and aVideo decompressor

are needed to see this picture.

Page 13: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

University of UtahUniversity 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”

Page 14: Interactive, GPU-Based Level Sets for 3D Segmentation Aaron Lefohn Joshua Cates Ross Whitaker University of Utah Aaron Lefohn Joshua Cates Ross Whitaker

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