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A Framework for Comparison and Evaluation of Nonlinear Intra-Subject Image Registration Algorithms-8396
 

A Framework for Comparison and Evaluation of Nonlinear Intra-Subject Image Registration Algorithms-8396

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http://hdl.handle.net/1926/561

http://hdl.handle.net/1926/561

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    A Framework for Comparison and Evaluation of Nonlinear Intra-Subject Image Registration Algorithms-8396 A Framework for Comparison and Evaluation of Nonlinear Intra-Subject Image Registration Algorithms-8396 Presentation Transcript

    • 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
    • Motivation Validation 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. Nonlinear Image Registration
    • Contents
      • Problem Definition
      • Related Work & Similar Efforts
      • Open Science Nonlinear Registration Evaluation Framework
      • Sample Evaluation – Intra-subject Thorax CT
      • Conclusion & Outlook
    • The Nonlinear Registration Problem
      • „ Find a deformable mapping h(x) aligning moving and fixed image such that a defined similarity criterium is minimized.“
      h(x) 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, …
    • 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 Volume Segmentation from MRI Images [Ng02] – taken from ITK Documentation Inter- & Intra-Modality Inter- & Intra-Subject No Ground Truth or Gold Standards Highly Ill-Posed How should we reach clinical acceptance?
    • Related Work on Evaluation (Frameworks) There exist some good ideas for evaluation in (Medical) Computer Vision! VALMET Segmentation Evaluation [Gerig et al] Community would benefit from open-source implementation! MICCAI 2007 Segmentation Challenge Workshop Multi-View 3D Reconstruction [Seitz et al] Stereo Reconstruction [Scharstein et al] Middlesbury Retrospective Evaluation of Intersubject Brain Registration [Hellier et al] Retrospective Rigid Registration Evaluation Project [West et al] NIREP (Inter-subject Brain-Registration Evaluation) [Christensen et al] Validation of Nonrigid Image Registration Using FEM [Schnabel et al] Segmentations are compared!
    • 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:
      Registration Algorithms Public domain data sets Synthetic Deformations Similarity Measures Python Framework as Glue
    • Open Science Evaluation Framework
    • 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
    • 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)
    • 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!
    • 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
    • A Sample Evaluation Simulated Data Difference to Original Standard 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
      • Visual Results (Problem Cases)
        • Level Set motion -> artifacts
        • Fast Block Matching -> Implementation Issues (Test Framework)
        • Symmetric Demons
    • 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…
    • 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“?
    • Thank you for your attention!
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