USP.ppt

B
Quantitative analysis of radiologic images:
Image segmentation and registration,
statistical atlases
Christos Davatzikos, Ph.D.
Professor of Radiology
Section of Biomedical Image Analysis
http://www.rad.upenn.edu/sbia
You can control a quantity if you can measure or weigh it
Lord Kelvin, 1824-1907
Need to develop tools that obtain accurate and
precise measurement from image data
Expert 1: Total Lesion volume: 15,635 mm^3 Expert 2: Total Lesion volume: 7,560 mm^3
Human limitations in measuring: inter-rater differences
Major limitation for:
1) Diagnosis of disease stage
2) Monitoring the effect of treatments
Quantification/measurement:
- ~3% longitudinal atrophy of the
hippocampus in early AD patients
- Contraction pattern of the cardiac muscle
- a 5% change in radiologic signal could be
indicative of evolving pathology
More human limitations
Visually detecting morphological abnormalities
Scan 1 Scan 2
Visually detecting morphological abnormalities
Scan 1 Scan 2
30% atrophy!
Manual Drawing of anatomical structures
Visual evaluation of a 3% atrophy is practically
impossible Laborious and not well-reproducible
manual outlining is required
•Evaluating complex spatio-temporal patterns of
radiologic signal change, especially if the
magnitude of the signal change is small and
anatomical variability is large
Kahneman and Tversky in their Nobel prize winninng
careers studied human reasoning under uncertainty and
demonstrated the limitations of human reasoning in
evaluating conjunctions, i.e. A and B and C …
Even more fundamental limitations of human evaluation
Detecting spatially complex very subtle
anatomical abnormalities
Normal Schizophrenia patient
?
Healthy Mildly Cognitively impaired:
Prodromal stage to Alzheimer’s
Detecting spatially complex very subtle
anatomical abnormalities
?
Functional activity during truth telling and lying
Lies
Truths
Brain and criminal behavior
-2
-1.8
-1.6
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
Total
Frontal
Left
Total
Frontal
Right
Total
Parietal
Left
Total
Parietal
Right
Total
Temporal
Left
Total
Temporal
Right
Total
Occipital
Left
Total
Occipital
Right
lateral
ventricle
left
lateral
ventricle
right
Computers can complement and assist
humans in many ways
Statistical anatomical atlases: from single-
individual anatomical examples, to atlases
capturing variability in a population
Analogous to training of a human reader
• Disease identification (learn variation of normal anatomy 
identify abnormality as a deviation from normal variation)
• Integration of data from multiple individuals in order to discover
systematic relationships among radiologic and clinical measurements
-Does a lesion in a particular part of the brain correlate with a
certain neurological deficit?
-Does prostate cancer appear uniformly throughout the prostate or
does it tend to appear in certain regions more frequently  what
is the optimal way of biopsying/treating a patient in order to
maximize probability of cancer detection/elimination?)
-What is the normal variation of hippocampal size for a given
age?
- What is the normal variation of cardiac shape and deformation?
Image Registration: Integration and Comparative Analysis of
Images from different individuals / modalities / times /conditions
Before
Spatial
Normalization
After
Spatial
Normalization
--Image integration and co-registration helps generalize
from the individual to the group, and to construct normative
data  abnormalities can be distinguished from normal
statistical variation
Underlying biological
process that results in
abnormal signal, or
simply normal tissue
whose normal variability,
in terms of image
properties, needs to be
measured
Overlay/Comparison
of such images?
Registration and Measurement of Biological Shape  D’Arcy Thompson, 1917:
• The deformation function measures the local deformation of the template:
Deformation 1 Deformation 2
Local structural measurements can be measured by analyzing the
deformation functions with standard statistical methodologies
Template Shape 1 Shape 2
Red: Contraction
Green:
Expansion
≈
High-Dimensional Shape Transformations
Template MR image Warped template
USP.ppt
Significant 4-year GM changes in 107 older adults
From the cover page of
the Lancet, Neurology
RIGHT LEFT
Voxel-based analysis of tissue density maps
Effect Size Maps
NC > FTD
NC > AD
FTD > AD
Tissue atrophy map of an AD patient, relative to
cognitively normal controls
Template Space
Patient’s scan
Regions of differences between
schizophrenics and normal controls
Average of 148 brain
images, after
deformable registration
to the atlas
Atlas with optimal
needle positions
Apex
Base
Left
Right
6
7
4
3
1
2
5
Apex
Base
Left Right
Targeted Prostate Biopsy Using Mathematical Optimization
100 Samples Template
…
Segmented 3D Prostate
Warped Prostate Atlas
US prostate image
MRI prostate image
Deformable Segmentation
of Prostate Images
20 subjects, average age 64.70 20 subjects, average age 83.05
Quantitative analysis
meets
visual image interpretation
40174 mm3
20564 mm3
“Younger Old Adult”
Average Model
“Older Old Adult”
Average Model
Average age 64.7 Average age 83
Using a statistical atlas to guide
WM lesion segmentation
Spatial distribution of WM
abnormalities in 50 older adults
(BLSA)
HAMMER: Hierarchical Attribute
Matching Mechanism for Elastic
Registration
Pattern Matching: Finding
Anatomical Correspondences
Attribute vector based on wavelet analysis of the anatomical context
around each voxel  morphological signature of each voxel
Template
A brain MRI before warping and after warping
Model
Measuring volumes of anatomical structures :
An atlas with anatomical definitions is registered to the patient’s images
Subject
HAMMER HAMMER
To summarize:
• Anatomical definitions are used to create an atlas 
analogous to the knowledge of anatomy by humans
• Pattern matching performed hierachically at various
scales is used to match the atlas to the individual
Can we use these quantitative image
analysis tools as diagnostic tools?
-Combine all morphological, physiological, and clinical
measurements into a broader phenotypic profile
-Use high-dimensional pattern classification and machine
learning techniques
Problem: Potentially high statistical
overlap for any single anatomical
structure, if disease is not focal
Where is the problem?
0.001
0.0012
0.0014
0.0016
0.0018
0.002
0.0022
0.0024
0.0026
0.0028
0.0017 0.0027 0.0037 0.0047
Hippocampus Volume
Entorhinal
Cortex
Volume
Normal Controls
MCI
Data from Baltimore Longitudinal Study of Aging, Davatzikos et.al.
Neurobiology of aging, in press
Pattern
Classification
Abnormality
score
A pattern is sampled by measuring brain volumes
and blood flow in a number of brain regions
• Local tissue volumes and PET O15 are combined
• 15-20 brain regions (clusters) build a multi-
parametric imaging profile
Abnormality Score
Measurement and Integration of Structural
and Functional Patterns
0.001
0.0012
0.0014
0.0016
0.0018
0.002
0.0022
0.0024
0.0026
0.0028
0.0017 0.0027 0.0037 0.0047
Hippocampus Volume
Entorhinal
Cortex
Volume
Normal Controls
MCI
Individual Diagnosis
• High-dimensional Pattern Classification (Machine learning)
• Evaluate spatial patterns of GM, WM, CSF, PET signal distribution
• Use these pattern to construct an image-based classifier, using
support vector machines
L-ERC
w
Anterior L-hipp
Brain regions that collectively contributed to classification
All GM
Effect size
WM
Effect size
PET
Effect size
Images in radiology
convention
Classification Rate vs. Number of Regions
Change of abnormality scores over time
* Clinically normal, has now gone
through autopsy with Braak 4 and
moderate plaques  meets AD
pathology criteria
*
After removing this one
participant
Normals: -0.3
MCI at latest scan: 0.26
MCI at year of conversion: 0.15
Already significant structural abnormality
on year of conversion to MCI
Abnormality scores when converting from normal to MCI
Data from ADNI
AD vs CN classifier applied to MCI: most MCI’s
have AD-like MRI profiles
MMSE
decline
fMRI for Lie Detection: A Card
Concealment Experiment
• Experiments performed by the Brain and Behavior Laboratory (Psychiatry)
• Particiapnts were asked to lie about the possession of a card of their choice
• 22 participants, both true/lie responses
• Parameter images were created using the GLM with double gamma HRF
Most discriminative brain region: 63.1%
Region1 /
Structure 1
Region 2/
Structure 2
Focal effects
Non-focal
effects
H
P
H
P
The power of true multi-variate analysis vs. mass-univariate
Training Results
-6
-4
-2
0
2
4
6
Decision
Values
Lie - 99.26%
T ruth - 99.27%
Testing Results
-6
-4
-2
0
2
4
6
1 21 41 61 81 101
Decision
Values
Lie - 90.00%
T ruth - 85.83%
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
1 3 5 7 9 11 13 15 17 19 21
Decision
Values
Lie - 95.5%
Truth - 95.5%
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
1 3 5 7 9 11 13 15 17 19 21
Decision
Values
Lie - 90.9%
Truth - 86.36%
Pattern classification results
Individual images
Average images
Lie
Truth
Set of regions with
predictive power
Statistical maps of group differences
Multi-variate analysis continued……..
…..combining different types of images
Image1
Image2
No single image says it all!
Computer result by combining 4
different MR acquisition protocols
Conclusion
Computers can complement humans in:
• Quantification
• Increased reproducibility
• Analysis of non-focal disease
• Evaluating complex spatio-temporal patterns
-patterns of longitudinal change of structure and
function
- patterns of tissue motion and deformation
In the heart of computational image analysis is the notion of
statistical atlases, which represent normal variation and help
identify disease as a deviation from this normal range
http://www.rad.upenn.edu/sbia
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USP.ppt

  • 1. Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology Section of Biomedical Image Analysis http://www.rad.upenn.edu/sbia
  • 2. You can control a quantity if you can measure or weigh it Lord Kelvin, 1824-1907 Need to develop tools that obtain accurate and precise measurement from image data
  • 3. Expert 1: Total Lesion volume: 15,635 mm^3 Expert 2: Total Lesion volume: 7,560 mm^3 Human limitations in measuring: inter-rater differences
  • 4. Major limitation for: 1) Diagnosis of disease stage 2) Monitoring the effect of treatments
  • 5. Quantification/measurement: - ~3% longitudinal atrophy of the hippocampus in early AD patients - Contraction pattern of the cardiac muscle - a 5% change in radiologic signal could be indicative of evolving pathology More human limitations
  • 6. Visually detecting morphological abnormalities Scan 1 Scan 2
  • 7. Visually detecting morphological abnormalities Scan 1 Scan 2 30% atrophy!
  • 8. Manual Drawing of anatomical structures Visual evaluation of a 3% atrophy is practically impossible Laborious and not well-reproducible manual outlining is required
  • 9. •Evaluating complex spatio-temporal patterns of radiologic signal change, especially if the magnitude of the signal change is small and anatomical variability is large Kahneman and Tversky in their Nobel prize winninng careers studied human reasoning under uncertainty and demonstrated the limitations of human reasoning in evaluating conjunctions, i.e. A and B and C … Even more fundamental limitations of human evaluation
  • 10. Detecting spatially complex very subtle anatomical abnormalities Normal Schizophrenia patient ?
  • 11. Healthy Mildly Cognitively impaired: Prodromal stage to Alzheimer’s Detecting spatially complex very subtle anatomical abnormalities ?
  • 12. Functional activity during truth telling and lying Lies Truths
  • 13. Brain and criminal behavior -2 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 Total Frontal Left Total Frontal Right Total Parietal Left Total Parietal Right Total Temporal Left Total Temporal Right Total Occipital Left Total Occipital Right lateral ventricle left lateral ventricle right
  • 14. Computers can complement and assist humans in many ways
  • 15. Statistical anatomical atlases: from single- individual anatomical examples, to atlases capturing variability in a population Analogous to training of a human reader
  • 16. • Disease identification (learn variation of normal anatomy  identify abnormality as a deviation from normal variation) • Integration of data from multiple individuals in order to discover systematic relationships among radiologic and clinical measurements -Does a lesion in a particular part of the brain correlate with a certain neurological deficit? -Does prostate cancer appear uniformly throughout the prostate or does it tend to appear in certain regions more frequently  what is the optimal way of biopsying/treating a patient in order to maximize probability of cancer detection/elimination?) -What is the normal variation of hippocampal size for a given age? - What is the normal variation of cardiac shape and deformation?
  • 17. Image Registration: Integration and Comparative Analysis of Images from different individuals / modalities / times /conditions Before Spatial Normalization After Spatial Normalization --Image integration and co-registration helps generalize from the individual to the group, and to construct normative data  abnormalities can be distinguished from normal statistical variation Underlying biological process that results in abnormal signal, or simply normal tissue whose normal variability, in terms of image properties, needs to be measured Overlay/Comparison of such images?
  • 18. Registration and Measurement of Biological Shape  D’Arcy Thompson, 1917:
  • 19. • The deformation function measures the local deformation of the template: Deformation 1 Deformation 2 Local structural measurements can be measured by analyzing the deformation functions with standard statistical methodologies Template Shape 1 Shape 2 Red: Contraction Green: Expansion ≈ High-Dimensional Shape Transformations Template MR image Warped template
  • 21. Significant 4-year GM changes in 107 older adults From the cover page of the Lancet, Neurology
  • 22. RIGHT LEFT Voxel-based analysis of tissue density maps Effect Size Maps NC > FTD NC > AD FTD > AD
  • 23. Tissue atrophy map of an AD patient, relative to cognitively normal controls Template Space Patient’s scan
  • 24. Regions of differences between schizophrenics and normal controls Average of 148 brain images, after deformable registration to the atlas
  • 25. Atlas with optimal needle positions Apex Base Left Right 6 7 4 3 1 2 5 Apex Base Left Right Targeted Prostate Biopsy Using Mathematical Optimization 100 Samples Template … Segmented 3D Prostate Warped Prostate Atlas US prostate image MRI prostate image Deformable Segmentation of Prostate Images
  • 26. 20 subjects, average age 64.70 20 subjects, average age 83.05 Quantitative analysis meets visual image interpretation 40174 mm3 20564 mm3 “Younger Old Adult” Average Model “Older Old Adult” Average Model Average age 64.7 Average age 83
  • 27. Using a statistical atlas to guide WM lesion segmentation Spatial distribution of WM abnormalities in 50 older adults (BLSA)
  • 28. HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration
  • 29. Pattern Matching: Finding Anatomical Correspondences Attribute vector based on wavelet analysis of the anatomical context around each voxel  morphological signature of each voxel
  • 30. Template A brain MRI before warping and after warping
  • 31. Model Measuring volumes of anatomical structures : An atlas with anatomical definitions is registered to the patient’s images Subject HAMMER HAMMER
  • 32. To summarize: • Anatomical definitions are used to create an atlas  analogous to the knowledge of anatomy by humans • Pattern matching performed hierachically at various scales is used to match the atlas to the individual
  • 33. Can we use these quantitative image analysis tools as diagnostic tools? -Combine all morphological, physiological, and clinical measurements into a broader phenotypic profile -Use high-dimensional pattern classification and machine learning techniques Problem: Potentially high statistical overlap for any single anatomical structure, if disease is not focal
  • 34. Where is the problem? 0.001 0.0012 0.0014 0.0016 0.0018 0.002 0.0022 0.0024 0.0026 0.0028 0.0017 0.0027 0.0037 0.0047 Hippocampus Volume Entorhinal Cortex Volume Normal Controls MCI Data from Baltimore Longitudinal Study of Aging, Davatzikos et.al. Neurobiology of aging, in press
  • 35. Pattern Classification Abnormality score A pattern is sampled by measuring brain volumes and blood flow in a number of brain regions • Local tissue volumes and PET O15 are combined • 15-20 brain regions (clusters) build a multi- parametric imaging profile
  • 36. Abnormality Score Measurement and Integration of Structural and Functional Patterns 0.001 0.0012 0.0014 0.0016 0.0018 0.002 0.0022 0.0024 0.0026 0.0028 0.0017 0.0027 0.0037 0.0047 Hippocampus Volume Entorhinal Cortex Volume Normal Controls MCI
  • 37. Individual Diagnosis • High-dimensional Pattern Classification (Machine learning) • Evaluate spatial patterns of GM, WM, CSF, PET signal distribution • Use these pattern to construct an image-based classifier, using support vector machines L-ERC w Anterior L-hipp
  • 38. Brain regions that collectively contributed to classification All GM Effect size WM Effect size PET Effect size Images in radiology convention
  • 39. Classification Rate vs. Number of Regions
  • 40. Change of abnormality scores over time * Clinically normal, has now gone through autopsy with Braak 4 and moderate plaques  meets AD pathology criteria * After removing this one participant
  • 41. Normals: -0.3 MCI at latest scan: 0.26 MCI at year of conversion: 0.15 Already significant structural abnormality on year of conversion to MCI Abnormality scores when converting from normal to MCI
  • 42. Data from ADNI AD vs CN classifier applied to MCI: most MCI’s have AD-like MRI profiles MMSE decline
  • 43. fMRI for Lie Detection: A Card Concealment Experiment • Experiments performed by the Brain and Behavior Laboratory (Psychiatry) • Particiapnts were asked to lie about the possession of a card of their choice • 22 participants, both true/lie responses • Parameter images were created using the GLM with double gamma HRF
  • 44. Most discriminative brain region: 63.1%
  • 45. Region1 / Structure 1 Region 2/ Structure 2 Focal effects Non-focal effects H P H P The power of true multi-variate analysis vs. mass-univariate
  • 46. Training Results -6 -4 -2 0 2 4 6 Decision Values Lie - 99.26% T ruth - 99.27% Testing Results -6 -4 -2 0 2 4 6 1 21 41 61 81 101 Decision Values Lie - 90.00% T ruth - 85.83% -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 1 3 5 7 9 11 13 15 17 19 21 Decision Values Lie - 95.5% Truth - 95.5% -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 1 3 5 7 9 11 13 15 17 19 21 Decision Values Lie - 90.9% Truth - 86.36% Pattern classification results Individual images Average images
  • 47. Lie Truth Set of regions with predictive power Statistical maps of group differences
  • 48. Multi-variate analysis continued…….. …..combining different types of images Image1 Image2 No single image says it all!
  • 49. Computer result by combining 4 different MR acquisition protocols
  • 50. Conclusion Computers can complement humans in: • Quantification • Increased reproducibility • Analysis of non-focal disease • Evaluating complex spatio-temporal patterns -patterns of longitudinal change of structure and function - patterns of tissue motion and deformation In the heart of computational image analysis is the notion of statistical atlases, which represent normal variation and help identify disease as a deviation from this normal range