tba #23 ge corporate r&d niskayuna, ny [email protected]
TRANSCRIPT
Unification of Vision, Geometry and Graphics
Through Toolkits
Bill Lorensen
GE Corporate R&D
Niskayuna, NY
What is a Toolkit?
Mathematics+
Algorithms+
Software
Edelsbrunner, 2001
Dual Interests
Marching Cubes 1984
Baseball Visualization 1989
Stream Polygons - 1991
Triangle Decimation - 1992
IEEE CG&A 1992
Swept Surfaces 1993
Removal Removal PathPath
Swept Swept SurfaceSurface
Virtual Endoscopy 1994
Creating Models from Segmented Medical Data
Surface and Volume Rendering
Hypothesis
Many real world problems cannot be solved by a single discipline
Core Technologies for 3D Medical Image Analysis
• Registration– Intra-modality (MRI to MRI, CT to CT)– Inter-modality (MRI to PET)– Model to Modality (Atlas to MRI)– Metadata to Modality (Clinical data,
biochip to MRI/CT)
• Filters– Edge preserving– Noise reduction– Non uniform intensity correction
• Segmentation– Edge detection– Region growing– Multi-channel
• Pattern Recognition– Tissue classification
• Visualization– Surface / volume rendering
– Fusion
• Quantification– Area, volume, shape
• Change detection– Longitudinal tracking
– Signal variation
• Information Analysis/Visualization
Discipline-specific Toolkits
• Use “best of breed” algorithms implemented by domain experts– Point matching– Voronoi diagram computation– Registration– Pose estimation– Isosurface extraction– Mathematical morphology– Skeletonization– Subdivision surfaces– Similarity measures– Surface simplification– Geometric compression
Discipline-specific Toolkits
• Examples– vtk, The Visualization Toolkit– Open Inventor, Graphics– Insight, Segmentation and Registration– CGAL, Computational Geometry– vxl, Image Understanding– Khoros, Image Processing
vtk, The Visualization Toolkit
• Open source toolkit for scientific visualization, computer graphics, and image processing
• C++ Class Library• 250,000 Lines of Code
– (~120,000 executable)• 20+ developers• 8 years of development• 1000 user mailing list
public.kitware.com/VTK
Insight Segmentation and Registration Toolkit
What is it?
• A common Application Programmers Interface (API).– A framework for software development– A toolkit for registration and segmentation– An Open Source resource for future research
• A validation model for segmentation and registration.– A framework for validation development– Assistance for algorithm designers– A seed repository for validated segmentations
Who’s sponsoring it?
The NationalScienceFoundation
The NationalInstitute for Dental and Craniofacial Research
The National Institute of Neurological Disorders and Stroke
$7.5 million, 3 year contract
Who’s creating it?
Contractor Roles
• GE CRD/Brigham and Womens– Architecture, algorithms, testing, validation
• Kitware– Architecture, user community support
• Insightful (formerly MathSoft)/UPenn– Statistical segmentation, mutual information registration, deformable
registration, level sets– Beta test management
• Utah– Level sets, low level image processing
• UNC/Pitt– Image processing, registration, high-dimensional segmentation
• UPenn/Columbia– Deformable surfaces, fuzzy connectedness, hybrid methods
Toolkit Requirements
• Shall handle large datasets– Visible Human data on a 512MB PC
• Shall run on multiple platforms– Sun, SGI, Linux, Windows
• Shall provide multiple language api’s• Shall support parallel processing• Shall have no visualization system
dependencies• Shall support multi-dimensional images• Shall support n-component data
Insight - Schedule
• Alpha Release, April 4, 2001.– Source code snapshot– Some non-consortium participation
• Limited Public Alpha Version, Aug 8, 2001.• Public Beta Release, December 15, 2001.• Software Developer’s Consortium Meeting
– Nov. 8-9, 2001, NLM, Bethesda.
www.itk.org
Testing Design
• Distributed testing– Developers and users must be able to easily
contribute testing results– Pulled together in a central dashboard
• Separate data from presentation• Cross-platform solution• Strive to have the same code tested in all
locations
Using vtk and Insight
Registration of Volumetric Medical Data
Mutual Information
• Computes “mutual information” between two datasets, a reference and target– MI(X,Y) = H(X) + H(Y) – H(X,Y)
• Small parameter set• Developed by Sandy Wells (BWH) and Paul Viola
(MIT) in 1995• Defacto standard for automatic, intensity based
registration
Insight Mutual Information Registration
• There is no MI open source implementation• The Insight Registration and Segmentation
Toolkit has an implementation• GE and Brigham as Insight contractors have
early access to the code• Code was developed at MathSoft (now called
Insightful)• GE was able to “guide” development with
input from Sandy Wells
Longitudinal MRI Study
• Register multiple volumetric MRI datasets of a patient taken over an extended time
• Create a batch processing facility to process dozens of datasets
• Resample the datasets
Approach
• Validate the algorithm• Pick a set of parameters that can be used
across all the studies• For each pair of datasets
– Perform registration– Output a transform
• View the resampled source dataset in context with the target dataset
Division of Labor
vtk
itk
vtk
Read data
Normalize data
Export data
Import Data
Register
Report transform
Read data
Reslice
Display
MRIRegistration.cxx
MultiCompare.tcl
The Pipeline
ImageReader ImageCast ImageShiftScale
ImageStatistics
ImageShrink3D
ImageExportImportImage
ImageToImageRigidMutualInformationGradientDescentRegistration
vnl_quaternion Matrix4x4
Oregon Data
• 25 Registrations• 13 Subjects• Qualitative comparison• One set of parameters for all studies
Longitudinal MRI No Registration
Checkerboard
SourceOriginalimage
Difference
TargetOriginal image
Longitudinal MRI Registration
Checkerboard
SourceOriginalimage
Difference
TargetOriginal image
Multi Field MRI Data
• Register 1.5T and 3T to 4T data• Resampled 1.5T and 3T to correspond to the
4T sampling• Volume rendering of the 3 datasets from the
same view
1.5T vs 4T MRI No Registration
Checkerboard
SourceOriginalImage
Difference
TargetOriginal Image
1.5T vs 4T MRI Registration
Checkerboard
SourceOriginalImage
Difference
TargetOriginal Image
3D Visualization of the same subjectScanned with different MR field Strengths
4T
3T 1.5T
All Registered To 4T
CT Lung Longitudinal Study
• Register two CT exams of the same patient taken at two different times
• Side-by-side synchronized view for visual comparison
Lung CT No Registration
Checkerboard
SourceOriginalImage
Difference
TargetOriginal Image
Lung CT Registration
Checkerboard
SourceOriginalImage
Difference
TargetOriginal Image
microPet/Volume CT
Back to the Software
Why Now?
• Internet enables distributed software development
• There are some successful Open Source projects
• A basic set of algorithms (and sometimes mathematics) exist
• Light weight software engineering processes exist– Low investment to support software development– Minimally invasive
Software Trends
Lightweight Software Engineering Processes
IEEE Computer October, 1999
Extreme Programming
Extreme Testing
Continuous Testing
Insight Project Management
• Robust code repository (cvs)• Active mailing list (mailman)• Automated documentation (doxygen)• Stable, cross platform build environment (cmake)• Weekly t-cons• Stable nightly build and test (300 builds)• Continuous build• Stable nightly dashboard (dart)• Quarterly face-to-face developer meetings• Semi-annual project meetings
Recipe for Success
• Vision
• Openness
• Community
• Strong core team
• Core Architecture
• Funding
Unification of Vision, Geometry and Graphics
Through Toolkits
Bill Lorensen
GE Corporate R&D
Niskayuna, NY