Autonomous Direct 3D Segmentation of Articular Knee Cartilage

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

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Outline

• Introduction
• Segmentation
• Discussion and results

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Introduction

• Osteoarthritis (OA) occurs
• 30 to 70 years
• Yearslt30High-impact sports player
• Using MRI
• High-contrast cartilage images
• Focus on
• Automation segmentation
• Improvement accuracy of cartilage measurements

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Introduction

• Expected outcomes
• Autonomous segmentation method
• Early detection of Pathology-Associated Changes
• Detection of early onset OA
• Problem
• Cant use only grey level features
• Similar cartilage contact zones
• Between the femoral and tibia cartilage

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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

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Introduction
8
Outline

• Introduction
• Segmentation
• Discussion and results

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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

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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

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Segmentation

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

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Segmentation

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

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Segmentation

• Match the cartilage contour as a partial
  differential level set equation
• ?f describes the normal velocity of the
  surface
• F defined range of surface deformations
• Match the cartilage contour

14
Outline

• Introduction
• Segmentation
• Discussion and results

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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