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Autonomous Direct 3D Segmentation of Articular Knee Cartilage

Autonomous Direct 3D Segmentation of Articular Knee Cartilage. Author :Enrico Hinrichs, Brian C. Lovell, Ben Appleton, Graham John Galloway Source : Australian and New Zealand Intelligent Information Systems , 10-12 December 1(1), pages 417-420, Sydney Speaker : Ren-LI Shen

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Autonomous Direct 3D Segmentation of Articular Knee Cartilage

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  1. Autonomous Direct 3D Segmentation of Articular Knee Cartilage Author :Enrico Hinrichs, Brian C. Lovell, Ben Appleton, Graham John Galloway Source :Australian and New Zealand Intelligent Information Systems, 10-12 December 1(1), pages 417-420, Sydney Speaker : Ren-LI Shen Advisor : Ku-Yaw Chang

  2. Outline • Introduction • Segmentation • Discussion and results

  3. Introduction • Osteoarthritis (OA) occurs • 30 to 70 years • Years<30:High-impact sports player • Using MRI • High-contrast cartilage images • Focus on • Automation segmentation • Improvement accuracy of cartilage measurements

  4. Introduction • Expected outcomes • Autonomous segmentation method • Early detection of Pathology-Associated Changes • Detection of early onset OA • Problem • Can’t use only grey level features • Similar cartilage contact zones • Between the femoral and tibia cartilage

  5. Introduction

  6. Introduction • Solution of these drawbacks is the main objective of this work • Develop a fully automated 3D segmentation • Non-linear diffusion(NLD) • Cartilage lesion classification system by Outerbridge

  7. Introduction

  8. Outline • Introduction • Segmentation • Discussion and results

  9. Segmentation • Previous work: B-Spline snakes • Develop a fully automated segmentation method • Using NLD and level sets • Articular cartilage is difficult to segment • It is a thin structure (1-2mm) • Another difficulty • Cannot be used to reliably cartilage degeneration • Multispectral Segmentation • Manual Segmentation

  10. Segmentation • Non-Linear Diffusion • Overcome meaningful details are removed as less important details • Enables image simplification • Preserves large intensity discontinuities and sharpens the edges of objects

  11. Segmentation • Non-Linear Diffusion • I is the image at time t and c is the diffusivity function

  12. Segmentation • Algorithm Development Using 3D Level Sets • Cartilage surface S is represented in space R3 • Three dimensional level set function φ maps to one dimension R

  13. Segmentation • Match the cartilage contour as a partial differential level set equation • |∇φ | describes the normal velocity of the surface • F defined range of surface deformations • Match the cartilage contour

  14. Outline • Introduction • Segmentation • Discussion and results

  15. Discussion and results • Automatic segmentation • Speed up drug development • Improve OA medication • The algorithm development is currently in the initial phase and more results will be provided soon

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