Focal Analysis of Knee
Articular Cartilage Quantity
and Quality
Project Meeting
ISBE, University of Manchester
Tuesday 21st September 2004
Objectives
Review current issues
 Agree critical path work plan items
 Determine deliverables






Minutes of Last Meeting
Current Status
Current Priorities and
Timeline
Specific Issues
Other Matters
Current
Status





Critical
Path
Options
Statistical shape models
CP78 Bone segmentations
Bone-Cartilage surface
registration
CP77
CP78
Methodology
Cartilages
Bones
Optimal Shape Model
Cartilage Thickness
Maps
Correspondences
Aggregate
Thickness Maps
Image
Segmentations
Registration
3D Surface
Statistical Analysis
CP77 Current Status
Optimal statistical shape
models (N19)
 Aggregate thickness maps
 Statistical analysis

Coverage
Mean
Left
Left
StdDev
Right
CP78 Current Status
Cartilage surface
construction
 Defining
correspondences

– Automatic bone
segmentation

Aggregate thickness
maps
– Preliminary Results
(N=9)
6 months
- baseline
Priorities and Timelines
CP77

Conclude Analysis 10+11/04
–
–
–
–




CP78

Assess model quality
Aggregate maps
Reproducibility
Class distinction
Literature trawl 11/04
Methodology 12/04
Further analysis
Data processing 10+11/04
– Bone segmentation
– Cartilage surface
construction
– Thickness maps

Statistical analysis 11/04
– Aggregate maps
– Reproducibility
– Disease progression
– 95% confidence intervals
– Multiple comparison test

Normal Range of Cartilage
Thickness

Disease Progression
Thickness Maps 12/04
Further Analysis
– Regions of interest
– Hypotheses testing
Optimal Statistical Shape Models

Current Status:
– Original methodology
– N19 Models
– Provide good correspondences


Critical Path:
Optional:
– Improved Methodology
• Integrate over the surface
– Correspondence Evaluation
• N30/N44 models – duplicate volunteer surfaces
• Surface registration and correspondence proximity
measurement
Statistical Shape Models
Femur
Correspondence
Patella
Correspondence
Tibia
Correspondence
CP78 Bone Segmentation

Current Status:
–
–
–

Manual segmentations
• Insufficient
• Use of LiveWire
AAM automatic segmentations
• Misalignment of patella
• Poor definition of Femur
and Tibia surfaces
Investigations in EndPoint
• Composite models
• Local shape warping
• Alternative model
parameters

Options:
–
–
–
–
Critical Path:
–
–
Set of CP78 bone
segmentations
CP78 bone correspondences
–
Employ current AAM search
segmentations
AAM built from larger data set
• N44 duplicates of
volunteers
Manually edit AAM search
segmentations and construct
shape models
Segment bone images
• Augment original
segmentations (patella
complete)  1 hour/seg.
• Segment one time point
and search remainder
using AAM  3 hours/seg.
• Segment complete set
Validation and evaluation
CP78 Manual Bone Segmentations
End slices not
segmented
Open
segments
LiveWire
segmentations
 1 hour per
segment to
correct
CP78 AAM Bone Segmentation


N19 Bone Models
Per-Bone AAM
searches
– Patella – high failure
rate (20%)

Composite Bone
Model
– Segment all bones
simultaneously
– More robust
– Loss of detail
Bone/Cartilage Surface Registration

Current Status:
– Composite surface registration
• Achieve consistent cortical bone thickness across all
surfaces
– CP77 results
– CP78 suspect, due to poor segmentations


Critical Path:
Options:
– Adopt current method
– Manual refinement of segmentations  ½ hour/seg.
– Investigate alternative methods
CP77 Surface Registration
Un-Registered
Registered
Un-Registered
Registered
CP77 Surface Registration
Un-Registered
Registered
Un-Registered
Registered
CP78 Surface Registration
Cartilage/Bone
registration
highlights
segmentation issues
Un-Registered
Registered
CP78 Surface Registration
Un-Registered
Registered
Un-Registered
Registered
CP77

Current Status:

– Corresponded thickness
maps
– Aggregate thickness maps

– Pointwise 95% confidence
intervals
– Multiple comparison tests
– Defining regions of interest
• On mean shape
• Meniscus windows
– Dominant leg analysis
Critical Path:
– N44/N30 model
construction
– Conclude analysis
• Model quality
• Reproducibility
• Class Distinction
– Literature trawl
Options:

Deliverables:
– Results
 Methodology
 Normal range of cartilage
thickness
CP77 Aggregate
Thickness Maps
Coverage
Mean
Min
Standard
Deviation
Max
Reproducibility Measures
Capture Variability in Thickness Maps
 Root Mean Square of Standard Deviation of
Thickness Measurements for each
Correspondence point
 Comparisons

– Inter-Segmentation
– Inter-Scanner & Inter-Scan

Examine CP77 volume BioStats analysis
– Mixed-effects models
Inter-Scanner
Inter-Segmentation


n = 19
4 Segs/Image (2 Nick, 2 Siân)



n=5
3 sites
1 cartilage seg/scan
Class Discrimination

Choice of analysis
–
–
–

Full data set analysis
• LDA – Two-way T-test
• Hotelling’s T2
Paired analysis
• PCA on difference maps
• Mahalanobis distance from origin
(zero change)
Randomisation tests to extract p-value
Analyses
–
–
–
Side Differences
• Left/Right
Cartilage Segmentors
• Nick/Siân
Inter-Site Comparisons
• Bristol/Liverpool
• Bristol/Manchester
• Liverpool/Manchester
Thickness Maps
n x p (104 x 9222)
PCA (Dimension
Reduction)
Model Parameters
n x n-1
Linear
Discriminant
Analysis
Visualise
Characteristic
Differences
Randomisation
Tests
p-value
(significance of
class
separation)
Side: Left/Right
Left (reflected)
Right
P<0.002
Inter-Segmentor: Nick/Siân
Inter-Site: Bristol/Liv
CP78 Analysis

Current Status:

– Preliminary Aggregate
thickness maps

– Replicate volume results for
verification
– Statistical analyses (1-6)
– Other hypotheses of disease
progression
– Defining regions of interest
• CP77 coverage maps
• Identified on model
Critical Path:
– Bone segmentations
– Identify correspondences
• thickness and signal
intensity analysis
– Cartilage surface
construction
• Same data set as used
for volumetric results
– Full data set processing
– Aggregate thickness maps
– Statistical analysis
• Reproducibility
• Anatomical hypothesis
of disease progression
Options:

Deliverables:
– Results
 CP78 thickness maps
CP78 Preliminary Results (n=9)
Coverage
Mean Difference
Thickness Maps
Anatomical Statistical Analyses
Test What?
Large ROI
e.g. Femoral
Cartilage
Volume
Small Anatomical
ROI
Statistical Approach
e.g. Central Medial
Tibial Plateau
Analysis 1 
Analysis 2
Univariate tests
“Has the cartilage volume
changed?”
Overall
Thickness
Map
Analysis 4
Analysis 3
Multivariate Tests
“Has the pattern of cartilage
thickness changed?
Thickness at
each
Location
Analysis 5
Analysis 6
Multiple Comparison Tests
“Where has the thickness
changed?”
Disease Progression Hypotheses
Anatomical
1.
–
Simultaneous analysis of thickness change at each and every
correspondence point.
Abnormality
2.
–
Assess thickness change in regions where cartilage is thin or thick
at baseline (in comparison with CP77).
Lesion proximity
3.
–
Assess thickness changes in regions surrounding a lesion in the
cartilage at baseline.
Opposition
4.
–
Assess thickness changes at locations which articulate with
regions that are abnormal at baseline.
Other Matters
Project Schedule/Contract
 Diurnal study
 Next meeting
