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http://hdl.handle.net/1926/561
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Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.20051
Brisbane, 02. Nov 2007 Intra-Modality Registration EvaluationMartin Urschler
A Framework for Comparison and Evaluation of Nonlinear Intra-Subject Image Registration
Algorithms
Martin Urschler, Stefan Kluckner, Horst Bischof
Institute for Computer Graphics and Vision,
Graz, University of Technology, Austria
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.20052
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Motivation
Nonlinear Image RegistrationValidation very difficult!
- Lack of direct ground truth
- Lack of gold standard methods
- Highly ill-posed problem
- Large space of possible solutions
We claim: Standardizing Evaluation Protocols at least as important as Developing Novel Methods!
-> Put framework to discussion:- Open-source community effort- Instantiate framework by showing sample evaluation
Presented algorithm at last years MICCAI.
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.20053
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Contents
• Problem Definition
• Related Work & Similar Efforts
• Open Science Nonlinear Registration Evaluation Framework
• Sample Evaluation – Intra-subject Thorax CT
• Conclusion & Outlook
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.20054
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
h(x)
The Nonlinear Registration Problem
„Find a deformable mapping h(x) aligning moving and fixed image such that a defined similarity criterium is minimized.“
Base Illustration taken from ITK Software Guide.
SSD, NCC, Mutual Information, …
B-Spline, Thin-Plate Spline, Def Field, …
Gradient Descent, BFGS, …
Intensity-Based, Feature-Based, …
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.20055
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Medical Applications of Nonlinear Image Registration
• Angiography Studies• Anatomy & Function
– Perfusion/Ventilation
• Correction of Motion Artifacts• Studies of Shape Variation• Segmentation by Atlas Registration• Surgical Planning• …
Lung Perfusion for Pulmonary Embolism Detection[Wildberger et al.]
Validation Study: Atlas-based Brain VolumeSegmentation from MRI Images[Ng02] – taken from ITK Documentation
Inter- & Intra-Modality Inter- & Intra-Subject
No Ground Truth or Gold StandardsHighly Ill-Posed
How should we reachclinical acceptance?
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.20056
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Related Work on Evaluation (Frameworks)
There exist some good ideasfor evaluation
in (Medical) Computer Vision!
Multi-View 3DReconstruction
[Seitz et al]Stereo
Reconstruction[Scharstein et al]
Middlesbury
VALMET Segmentation Evaluation [Gerig et al]
Retrospective Evaluation of Intersubject Brain Registration [Hellier et al]
Retrospective Rigid Registration Evaluation Project [West et al]
NIREP (Inter-subject Brain-RegistrationEvaluation) [Christensen et al]
Validation of Nonrigid Image
Registration Using FEM [Schnabel et al]
Segmentations are compared!
Community would benefitfrom open-sourceimplementation!
MICCAI 2007 SegmentationChallenge Workshop
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.20057
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Open Science Evaluation Framework
• Algorithm Evaluation vs. Validation [Hellier et al]– Specific Problems: Lacking ground truth, ill-posedness, Lack of gold standard – General Problems: Noise, Partial Volume Effect, Interpolation, Numerics
• Modular Evaluation Framework– Open-source, open-access, open-data, open protocols
• Building Blocks:
RegistrationAlgorithms
Public domaindata sets
Synthetic Deformations
Similarity MeasuresPython Framework
as Glue
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.20058
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Open Science Evaluation Framework
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.20059
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Synthetic Deformations Used
• Simple synthetic transformation models– Regular grid with random deformations & TPS– Uniform periodic cosine transformation
– Tailored to breathing difference registration:• Simulated Breathing Model
• Synthetic Airway Tree Movement from Manual Correspondences
• …
• Increase number of models– Pool of synthetic transformations defined
by pool of algorithms („bronze standard“ Glatard et al – MICCAI 2006)
– Community effort needed
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.200510
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Data Sets and Algorithms
• Public domain data sets from – National Library of Medicine Dataset Collection (currently offline)
– MIDAS data collection project
– National Lung Cancer Archives (NCIA)
• ITK Algorithms– Demons
– Symmetric Demons
– Level Set Motion
– Curvature
– Fast Block Matching (Workshop Contribution)
– Diffeomorphic Demons (Workshop Contribution)
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.200511
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Quantitative Measures
• Measures on Displacement Fields– RMS of displacement field differences– MAD of displacement field differences– MAX of displacement field differences– Jacobian determinant of displacement field
• Measures on fixed and warped moving image– Clamped RMS intensity differences– MAD intensity differences– MAX intensity differences– Normalized Mutual Information– Edge Overlap
• IMHO only first group says something about registration performance!
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.200512
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
A Sample Evaluation• Purpose: Evaluate intra-modality thoracic CT registration
subject to breathing differences
• 2 data sets 256^3– NLM NormalChestCTNoContrast– NCIA LIDC 30047
• Single choice of algorithm parameters• 64 bit Opteron with 2.4GHz and 8GB RAM
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.200513
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
A Sample Evaluation
Simulated Data Difference to Original Standard Demons
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.200514
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
• Visual Results (Problem Cases)– Level Set motion -> artifacts– Fast Block Matching ->
Implementation Issues (Test Framework)
– Symmetric Demons
A Sample Evaluation - Results
• Quantitative measures– Large number (see paper)– Quantities are single numbers
• Problematic?
– Standard Demons & Diffeomorphic Demons very well
– Algorithm Parameter Studies
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.200515
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Conclusion
• Open Science Evaluation Framwork presented• Intra-Modality Sample Evaluation shown
– Results should not be seen as final algorithm quality statements• Framework has to grow…
– Useful for comparing & testing algorithms
– Useful for parameter studies
• A small step towards establishing standardized protocol to gain clinical acceptance…
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.200516
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Further Work
• Common Web Repository (Hosted by Kitware?)• Upgrade by community effort
– More algorithms
– More quality measures • Segmentation based -> Problems for general applicability
– More synthetic deformations, including noise models
• Extension to inter-modality, inter-subject problems• Cooperate with NIREP?• How to prevent „evaluation framework tuning“?
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.200517
Brisbane, 02. Nov 2007 Intra-Modality Registration EvaluationMartin Urschler
Thank you for your attention!
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.200518
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.200519
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
Graz, University of Technology, Institute for Computer Graphics and Vision
Professor Horst Cerjak, 19.12.200520
Brisbane, 02. Nov 2007 Nonlinear Registration EvaluationMartin Urschler
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