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Page 1: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

Page 2: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

Page 3: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

Page 4: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Major limitation for:

1) Diagnosis of disease stage

2) Monitoring the effect of treatments

Page 5: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

Page 6: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

 

Visually detecting morphological abnormalities

Scan 1 Scan 2

Page 7: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

 

Visually detecting morphological abnormalities

Scan 1 Scan 2

30% atrophy!

Page 8: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Manual Drawing of anatomical structures

Visual evaluation of a 3% atrophy is practically impossible Laborious and not well-reproducible manual outlining is required

Page 9: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

•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

Page 10: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Detecting spatially complex very subtle anatomical abnormalities

Normal Schizophrenia patient

?

Page 11: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Healthy Mildly Cognitively impaired:Prodromal stage to Alzheimer’s

Detecting spatially complex very subtle anatomical abnormalities

?

Page 12: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Functional activity during truth telling and lying

Lies

Truths

Page 13: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Brain and criminal behavior

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-1.8

-1.6-1.4

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TotalFrontal

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TotalFrontalRight

TotalParietal

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TotalParietalRight

TotalTemporal

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TotalTemporal

Right

TotalOccipital

Left

TotalOccipital

Right

lateralventricle

left

lateralventricle

right

Page 14: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Computers can complement and assist humans in many ways

Page 15: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Statistical anatomical atlases: from single-individual anatomical examples, to atlases

capturing variability in a population

Analogous to training of a human reader

Page 16: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

• 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?

Page 17: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Image Registration: Integration and Comparative Analysis of Images from different individuals / modalities / times /conditions

BeforeSpatialNormalization

AfterSpatialNormalization

--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?

Page 18: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Registration and Measurement of Biological Shape D’Arcy Thompson, 1917:

Page 19: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

• 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: ContractionGreen: Expansion

High-Dimensional Shape Transformations

Template MR image Warped template

Page 20: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology
Page 21: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Significant 4-year GM changes in 107 older adults

From the cover page of

the Lancet, Neurology

Page 22: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

RIGHT LEFT

Voxel-based analysis of tissue density maps

Effect Size Maps

NC > FTD

NC > AD

FTD > AD

Page 23: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Tissue atrophy map of an AD patient, relative to cognitively normal controls

Template Space

Patient’s scan

Page 24: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Regions of differences between schizophrenics and normal controls

Average of 148 brain images, after deformable registration to the atlas

Page 25: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

Page 26: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

20 subjects, average age 64.70 20 subjects, average age 83.05

Quantitative analysis meets

visual image interpretation

40174 mm320564 mm3

“Younger Old Adult” Average Model

“Older Old Adult” Average Model

Average age 64.7 Average age 83

Page 27: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Using a statistical atlas to guide WM lesion segmentation

Spatial distribution of WM abnormalities in 50 older adults

(BLSA)

Page 28: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

HAMMER: Hierarchical Attribute Matching Mechanism for Elastic

Registration

Page 29: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Pattern Matching: Finding Anatomical Correspondences

Attribute vector based on wavelet analysis of the anatomical context around each voxel morphological signature of each voxel

Page 30: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Template

A brain MRI before warping and after warping

Page 31: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Model

Measuring volumes of anatomical structures :An atlas with anatomical definitions is registered to the patient’s images

Subject

HAMMER HAMMER

Page 32: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

Page 33: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

Page 34: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

En

torh

inal

Co

rtex

Vo

lum

e

Normal Controls

MCI

Data from Baltimore Longitudinal Study of Aging, Davatzikos et.al. Neurobiology of aging, in press

Page 35: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

Page 36: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

En

torh

ina

l Co

rte

x V

olu

me

Normal Controls

MCI

Page 37: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

PE

T-po

st c

ingu

late

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-E

RC

w

Anterior L-hipp

Page 38: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Brain regions that collectively contributed to classification

All GMEffect size

WM Effect size

PETEffect size

Images in radiology convention

Page 39: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Classification Rate vs. Number of Regions

Page 40: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

Page 41: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

Page 42: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Data from ADNI

AD vs CN classifier applied to MCI: most MCI’s have AD-like MRI profiles

MMSE decline

Page 43: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

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

Page 44: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Most discriminative brain region: 63.1%

Page 45: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Region1 /Structure 1

Region 2/Structure 2

Focal effects

Non-focal effects

H

P

HP

The power of true multi-variate analysis vs. mass-univariate

Page 46: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Training Results

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Lie - 99.26%Truth - 99.27%

Testing Results

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Lie - 90.00%Truth - 85.83%

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Decis

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Lie - 95.5%Truth - 95.5%

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Lie - 90.9%

Truth - 86.36%

Pattern classification results

Individual images

Average images

Page 47: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Lie

Truth

Set of regions with predictive power

Statistical maps of group differences

Page 48: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Multi-variate analysis continued……..

…..combining different types of images

Image1

Image2

No single image says it all!

Page 49: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

Computer result by combining 4 different MR acquisition protocols

Page 50: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

ConclusionComputers 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

Page 51: Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology

http://www.rad.upenn.edu/sbia


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