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Tricia Pang November 25, 2008

Three-Dimensional Human Airway Segmentation for Sleep Apnea Diagnosis using Tubular Deformable Organisms. Tricia Pang November 25, 2008. OVERVIEW. Motivation Approach Preliminary Investigation Deformable Organisms Preliminary Results Conclusion. OVERVIEW. Motivation Approach

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Tricia Pang November 25, 2008

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  1. Three-Dimensional Human Airway Segmentation for Sleep Apnea Diagnosisusing Tubular Deformable Organisms Tricia Pang November 25, 2008

  2. OVERVIEW • Motivation • Approach • Preliminary Investigation • Deformable Organisms • Preliminary Results • Conclusion

  3. OVERVIEW • Motivation • Approach • Preliminary Investigation • Deformable Organisms • Preliminary Results • Conclusion

  4. Motivation • Obstructed sleep apnea (OSA) disorder • Caused by collapse of soft tissue walls in the airway → model patient's airway to help diagnosis • Hand-segmentation: laborious • Goal: to develop automated tool for creating a patient-specific model of the airway Credit: Wikipedia

  5. Motivation • Artisynth [2] &OPAL Project(OPAL = Dynamic Modeling of the Oral, Pharyngeal and Laryngeal Complex for Biomedical Engineering) • Import resulting airway into dynamic throat and mouth model for simulation

  6. OVERVIEW • Motivation • Approach • Preliminary Investigation • Deformable Organisms • Preliminary Results • Conclusion

  7. Data Source - MRI • Normal subjects, OSA patients, various treatments • Volumetric and cross-sectional measurements

  8. OVERVIEW • Motivation • Approach • Preliminary Investigation • Deformable Organisms • Preliminary Results • Conclusion

  9. Preliminary Investigation • Combined 2D segmentation of axial slices in Matlab • Procedure: • User-indicated start point at base of airway • Starting on axial slice at start point, grow ellipse outward • Iterate on all axial slices moving upwards along airway, and use previous segmentation as starting contour • “Active contours without edges” (Chan-Vese) [1]: • Based on Mumford-Shah framework • Evolve curve by minimizing energy from image (interior/exterior mean) and curvature

  10. OVERVIEW • Motivation • Approach • Preliminary Investigation • Deformable Organisms • Preliminary Results • Conclusion

  11. Deformable Organism • I-DO: framework for ITK (McIntosh & Hamarneh) [4] • Geometrical and physical layers of classical deformable models (data-driven) • Behavioral and cognitive layers for intelligent deformation control (knowledge-driven) • Related work: • Spinal crawler [5] • Vessel crawler [6]

  12. Deformable Organism • Goal: automatically segment airway by growing a tubular organism, guided by image data and a priori anatomical knowledge • Advantages: • Increased accuracy • Analysis and labeling capabilities • Ability to incorporate shape-basedprior knowledge • Modular framework

  13. Deformable Organism Framework

  14. Deformable Organism Framework

  15. Deformable Organism Framework

  16. Deformable Organism Framework

  17. Deformable Organism Framework

  18. Deformable Organism Framework

  19. Deformable Organism Framework

  20. Summary of Layers Control Center Grow, terminate, (branch) Sensors ‘GrowSense’ ‘HessianSense’ (‘BranchSense’) Behavior Grow, fit, (branch) Physics/Deformation Spring-mass system Medial and boundary nodes Radial, circumferential and sheer springs Geometric Medial-based shape representation Tubular with symmetric cross-section (often elliptical)

  21. Viewer Adaptor • Graphical interface for viewing geometry of DOs and their deformations in real time

  22. OVERVIEW • Motivation • Approach • Preliminary Investigation • Deformable Organisms • Preliminary Results • Conclusion

  23. OVERVIEW • Motivation • Approach • Preliminary Investigation • Deformable Organisms • Preliminary Results • Conclusion

  24. Summary • Model of a patient’s airway valuable to diagnosing the OSA disorder • Tubular deformable organisms • spring-mass system initiated at a user-indicated point • grown along the airway boundary using a priori knowledge of upper airway anatomy

  25. References [1] Chan, T. and Vese L. Active Contours Without Edges. IEEE Transactions on Image Processing, 10 (2001) [2] Fels, S., et al. Artisynth: A biomechanical simulation platform for the vocal tract and upper airway. International Seminar on Speech Production (2006) [3] Hamarneh, G. and McIntosh, C. Physics-Based Deformable Organisms for Medical Image Analysis. Proc of SPIE 5747 (2005) 326-335 [4] McIntosh, C. and Hamarneh, G. I-DO: A “Deformable Organisms” framework for ITK. Medical Image Analysis Lab, SFU. Release 0.50. [5] McIntosh, C. and Hamarneh, G. Spinal Crawlers: Deformable Organisms for Spinal Cord Segmentation and Analysis. MICCAI (2006) 808–815 [6] McIntosh, C. and Hamarneh, G. Vessel Crawlers: 3D Physically-based Deformable Organisms for Vasculature Segmentation and Analysis. Proceedings of IEEE CVPR (2006)

  26. Thank you!Questions?

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