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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:
Constructing Image Graphs for Segmenting Lesions in Brain MRI 3
Outline
• Background– Goal– Image Graph– Previous Work– Overview
• Methodology• Results• Conclusions and Future Work
Constructing Image Graphs for Segmenting Lesions in Brain MRI 4
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 5
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 6
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 7
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 8
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 9
Method Overview
WM GM CSF LesionAtlas based
training
Data driven clustering + anatomy
A1 A2 A3
v1 v2 v3Bayesian classification
Top-down
Bottom-up interface
Constructing Image Graphs for Segmenting Lesions in Brain MRI 10
Outline
• Background• Methodology• Results• Conclusions and Future Work
Constructing Image Graphs for Segmenting Lesions in Brain MRI 11
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 12
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 13
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 14
• 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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 15
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)
Constructing Image Graphs for Segmenting Lesions in Brain MRI 16
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 17
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 18
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 19
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 20
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 21
Bias Correction
• MR images present intensity inhomogeneities or bias fields (“vignetting”)
• Bias corrected using polynomial fit
Polynomial Fit
Constructing Image Graphs for Segmenting Lesions in Brain MRI 22
Method Summary
CSF
A2
v2
Bias Correction
Constructing Image Graphs for Segmenting Lesions in Brain MRI 23
Outline
• Background• Methodology• Results• Conclusions and Future Work
Constructing Image Graphs for Segmenting Lesions in Brain MRI 24
Duke C1011A3 Depression Study
Low contrast MRI
Constructing Image Graphs for Segmenting Lesions in Brain MRI 25
Voxel Only vs Hierarchical Classification
Low tissue contast Duke C1011A3 data:
FLAIR Voxel-only Hierarchical
Constructing Image Graphs for Segmenting Lesions in Brain MRI 26
Duke C1011
voxel only
hierarchical
Constructing Image Graphs for Segmenting Lesions in Brain MRI 27
Duke CRC-Oct04 (Aging/Depression)
Constructing Image Graphs for Segmenting Lesions in Brain MRI 28
Duke CRC
Constructing Image Graphs for Segmenting Lesions in Brain MRI 29
Multi-channel Segmentation
T1w T2w Flair Labels
Segmentation uses signature of all channels combined, using user-specified rules.
Constructing Image Graphs for Segmenting Lesions in Brain MRI 30
Outline
• Background• Methodology• Results• Conclusions and Future Work
Constructing Image Graphs for Segmenting Lesions in Brain MRI 31
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 32
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
Constructing Image Graphs for Segmenting Lesions in Brain MRI 33
Example NPSLE Lesion
Hypointense on T1 Hyperintense T2 Hyperintense on FLAIR
H Jeremy Bockholt , Charles GasparovicThe MIND Institute / UNMAlbuquerque, NM
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