autonomous direct 3d segmentation of articular knee cartilage
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Autonomous Direct Autonomous Direct 3D Segmentation of 3D Segmentation of Articular Knee CartilageArticular Knee Cartilage
Author :Enrico Hinrichs, Brian C. Lovell, Ben Appleton, Graham John
GallowaySource :Australian and New Zealand Intelligent Information Systems, 10-12 December 1(1), pages 417-420, SydneySpeaker : Ren-LI ShenAdvisor : Ku-Yaw Chang
1
OutlineIntroductionSegmentation Discussion and results
2
IntroductionIntroductionOsteoarthritis (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
3
IntroductionIntroductionExpected 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
4
IntroductionIntroduction
5
IntroductionIntroductionSolution 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
6
IntroductionIntroduction
7
OutlineIntroductionSegmentation Discussion and results
8
SegmentationSegmentationPrevious work: B-Spline snakesDevelop 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 Segmentation9
SegmentationSegmentationNon-Linear Diffusion
◦Overcome meaningful details are removed as less important details
◦Enables image simplification◦Preserves large intensity
discontinuities and sharpens the edges of objects
10
SegmentationSegmentationNon-Linear Diffusion
I is the image at time t and c is the diffusivity function
11
SegmentationSegmentationAlgorithm Development Using 3D
Level Sets◦Cartilage surface S is represented in
space R3
◦Three dimensional level set function φ maps to one dimension R
12
SegmentationSegmentationMatch 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
13
OutlineIntroductionSegmentation Discussion and results
14
Discussion and resultsDiscussion and resultsAutomatic segmentation
◦Speed up drug development◦Improve OA medication
The algorithm development is currently in the initial phase and more results will be provided soon
15