Constructing Image Graphs for Segmenting Lesions in Brain MRI May 29, 2007 Marcel Prastawa, Guido...

Constructing Image Graphs for Segmenting Lesions in Brain MRI

May 29, 2007

Marcel Prastawa, Guido Gerig

Department of Computer Science

UNC Chapel Hill

Constructing Image Graphs for Segmenting Lesions in Brain MRI 2

Goal

• Segmentation of “lesions”:– Abnormal tissue associated with neurodegeneration– Small patches

• Clinical applications: lupus (NAMIC), MS, aging, depression, NF1– Different appearances, locations, and shapes– Method needs to be adaptable

• Example:


Outline

• Background– Goal– Image Graph– Previous Work– Overview

• Methodology• Results• Conclusions and Future Work


Challenges

• Lesions are relatively small• Wide variety of shape• Partial voluming can be confused as lesions• Requires knowledge of neighboring structures• Voxel classification typically fails• Common MRF scheme oversmooths segmentation, hard to

balance

Proposed solution: Use a hierarchical graph representation


Manage hierarchical information

Image Graph

WM GM CSF LesionObject

Atom / Supervoxel /

Neighborhood

Voxel

A1 A2 A3

v1 v2 v3

Segmentation = determining info at nodes and edges


Previous Work [1/3]

[Barbu et al, PAMI 2005]• Image segmentation by graph clustering

• Group similar regions using Swendsen-Wang cuts• For natural images, no anatomical prior


Previous Work [2/3]

[Corso, Zhuowen Tu*, et al, IPMI 2007 (UCLA Loni)]• Segmentation of subcortical structures through graph shifts

• Training using boosting• No pathological class

*DDMCMC discriminative model guided generative model computing


Previous Work [3/3]

• Marcel Prastawa PhD: “An MRI Segmentation Framework for Brains with Anatomical Deviations”– EMS modulated by probabilistic brain atlas:– Nonparametric statistics– Robust clustering– Separation of pathology from healthy (tumor, edema, myelination, ..)– ITK implementation: GUI and XML scripts for large throughput– Rigorous validation (repeatability, validity, traveling phantom etc.)– Tested on over 1500 brain MRI

1. Marcel Prastawa, John H. Gilmore, Weili Lin, Guido Gerig, Automatic Segmentation of MR Images of the Developing Newborn Brain, Medical Image Analysis Vol 9, October 2005, pages 457-466

2. John H. Gilmore, Weili Lin, Marcel W. Prastawa, Christopher B. Looney, Y. Sampath K. Vetsa, Rebecca C. Knickmeyer, Dianne Evans, J. Keith Smith, Robert M. Hamer, Jeffrey A. Lieberman, Guido Gerig, Cerebral Asymmetry, Sexual Dimorphism, and Regional Gray Matter Growth in the Neonatal Brain, Accepted by J of Neuroscience, Oct 2006

3. Bénédicte Mortamet, Donglin Zeng, Guido Gerig, Marcel Prastawa, and Elizabeth Bullitt. Effects of Healthy Aging Measured By Intracranial Compartment Volumes Using a Designed MR Brain Database. Lecture Notes in Computer Science LNCS 3749, Oct. 2005, pp. 383 -- 391

4. Marcel Prastawa, Elizabeth Bullitt, Sean Ho, Guido Gerig, A Brain Tumor Segmentation Framework Based On Outlier Detection, Medical Image Analysis Vol. 8, Issue 3, Sept. 2004, pages 275-283

5. Marcel Prastawa, John Gilmore, Weili Lin, and Guido Gerig, Automatic Segmentation of Neonatal Brain MRI, Lecture Notes in Computer Science LNCS 3216, Springer Verlag, pp. 10-17, 2004

6. Marcel Prastawa, Elizabeth Bullitt, Nathan Moon, Koen van Leemput, and Guido Gerig, Automatic Brain Tumor Segmentation by Subject Specific Modification of Atlas Priors. Academic Radiology, Vol. 10 pp. 1341-1348 Dec. 2003

7. Marcel Prastawa, Elizabeth Bullitt, Sean Ho, Guido Gerig, Robust Estimation for Brain Tumor Segmentation, Lecture Notes in Computer Science LNCS 2879, pp. 530-537, Nov. 2003


Method Overview

WM GM CSF LesionAtlas based

training

Data driven clustering + anatomy

A1 A2 A3

v1 v2 v3Bayesian classification

Top-down

Bottom-up interface


Outline

• Background• Methodology• Results• Conclusions and Future Work


Object Level

• Training based on prior knowledge: brain atlas

• Use for sampling and as priors• No lesion model

– Lesion prior = fraction of wm or gm priors

WM GM CSF Lesion

ICBM/MNI atlas, average of 152 healthy adult subjects


Outlier Detection

• Lesion training data obtained via outlier detection• Robust estimation using MCD (minimum covariance determ.)• WM example:

• Use outlier samples that fit user defined rule for lesion

T1

T2

before after


Lesion Rules

• User defined rule for different lesion [van Leemput, TMI 2001]• Embedded Python interpreter, any function with variables for

voxel data (i1, … in) and training data (mu1_1 … mu#d_#c)• Example rules:

– MS lesion for [T1, T2, FLAIR]:

(i2 > mu2_2) and (i3 > mu3_1) and (i3 > mu3_2)

Radiology terms: Lesion is brighter than gm in T2, brighter than wm in Flair, and lesion is brighter than gm in Flair– NF1 lesion for [T1, T2, PD]: i2 > mu2_2

• Can use arithmetic:

(i2/i3 > mu2_2/mu3_2)• Adaptable: input parameter, can have user def. functions, etc


• Atom: group of voxels that are perceptually similar• Group neighboring voxels that:

1. Look similar

2. Located close to each other

3. Belong to the same category

• Combining 1, 2 leads to

data-driven schemes

Atom Assignments

CSF

A1 A2 A3

v1 v2 v3


Initial Voxel Grouping

• Group voxels that are similar and close to each other• Use watershed algorithm:

• Input for watershed transform is gradient magnitude image

(pictures from Matlab tutorial manual)


Multimodal Image Gradient

[Lee & Cok, IEEE TSP 1991] on gradients of vector field• Use largest singular value of Jacobian matrix

(DTI analogy: use λ1 vs MD)

• Example gradient image:

z

I

y

I

x

Iz

I

y

I

x

Iz

I

y

I

x

I

J

333

222

111


Communication between different levels in the hierarchy:

Information Flow

CSF

A2

v2

1. Appearance parameters (mean, covar)

2. Atlas priors1. Boundary

adjustments

2. Split / merge atoms

Appearance parameter adjustments


Object-Atom and Object-Voxel Interface

• Object passes down intensity parameters and

atlas priors• In atom level, image represented as flat patches• Compute class posterior probabilities of each voxel

and atom

CSF

A2

v2

'

)'Pr(),'|(

)Pr(),|(),|(

ckkk

kkkkk cScSIp

cScSIpcISp


Voxel-Atom Interface

• Change voxel grouping based on anatomy• Possible adjustments:

– Split/merge voxel groups– Boundary shift

• Split / merge not yet implemented (clustering)• Boundary shift:

– Every voxel in boundary between atoms get assigned to

atom with nearest Kullback-Leibler (KL) distance– Simulate region competition (SNAP)

CSF

S2

v2


Atom-Object Interface

• Atom posteriors determine classification of every

child voxel• May have conflict between voxel and atom

classication• Resolve by adjusting global parameters• Rationale: similar voxels must have the same

classification, if not then need to accommodate

violating voxels• Currently implemented by adjusting covariance

CSF

S2

v2


Bias Correction

• MR images present intensity inhomogeneities or bias fields (“vignetting”)

• Bias corrected using polynomial fit

Polynomial Fit


Method Summary

CSF

A2

v2

Bias Correction


Outline



Duke C1011A3 Depression Study

Low contrast MRI


Voxel Only vs Hierarchical Classification

Low tissue contast Duke C1011A3 data:

FLAIR Voxel-only Hierarchical


Duke C1011

voxel only

hierarchical


Duke CRC-Oct04 (Aging/Depression)


Duke CRC


Multi-channel Segmentation

T1w T2w Flair Labels

Segmentation uses signature of all channels combined, using user-specified rules.


Outline



Conclusions

• Segmentation using hierarchical scheme• Integrate top-down atlas-based approach and bottom-up data

driven approach• Segments small abnormal regions• OK results on obvious high contrast lesions


Future Work

• Splitting / merging of atoms• Improve classification scheme using non-parametric kernel

densities• Improve global parameter adjustment scheme• Partial voluming?

• Tests/Adapt to lesions in NAMIC MIND DBP (lupus)• Validation


Example NPSLE Lesion

Hypointense on T1 Hyperintense T2 Hyperintense on FLAIR

H Jeremy Bockholt , Charles GasparovicThe MIND Institute / UNMAlbuquerque, NM

Constructing Image Graphs for Segmenting Lesions in Brain MRI May 29, 2007 Marcel Prastawa, Guido...

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