Dinggang Shen

Development and Dissemination of

Robust Brain MRI Measurement Tools

(1R01EB006733)

Department of Radiology and BRICUNC-Chapel Hill

IDEA

Team

• UNC-Chapel Hill

- Dinggang Shen

- 1/2 Postdoctoral fellow(s)

• UPenn

- Christos Davatzikos

• GE

- Jim Miller

- Xiaodong Tao

Goal of this project

• To further develop HAMMER registration and white matter lesion (WML) segmentation algorithms, for improving their robustness and performance.

• To design separate software modules for these two algorithms and incorporate them into the 3D Slicer.

PACS Database Format

Converter

WML Segmentation Algorithm

Visualization Engine

Tissue Classification

HAMMER Complexity

Levels

Parameter Tuning

Learn Best Features

Deformation Constraints

Models

Tissue Density Maps

ROI Labeling

Group Analysis

ROI-based Analysis

HAMMER Registration Algorithm

Skull Stripping

SPM

Data importer

Data processing

Registration

Applications

Training

Skull Stripping

Multimodality Registration

Intensity Normalization

Manual Segmentation

Training SVM Classifier

Voxel-wise Segmentation

False-Positive Elimination

Application

MI

Q-MI

WML Atlas

Data processing

Overview of Our Brain Measurement Tools

• To further develop HAMMER registration and WML segmentation algorithms, for improving their robustness and performance.

• To design separate software modules for these two algorithms and incorporate them into the 3D Slicer.

Matching attribute vectors

Image registration and warping

Shen, et al., “HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration”, IEEE Trans. on Medical Imaging, 21(11):1421-1439, Nov 2002. (2006 Best Paper Award, IEEE Signal Processing Society)

HAMMER

Model:Individual:

(1) Formulated as correspondence detection

Registration – HAMMER

Difficulty: High variations of brain structures

How can we detect correspondences?

Solution: Use both global and local image features to represent anatomical structures, such as using wavelets or geometrical moments.

Xue, Shen, et al., “Determining Correspondence in 3D MR Brain Images Using Attribute Vectors as Morphological Signatures of Voxels”, IEEE Trans. on Medical Imaging, 23(10): 1276-1291, Oct 2004.

Distinctive character of attribute vector:

toward an anatomical signature of every voxel

Examples of attribute vector similarity maps, and point correspondences

Brain A Brain B Similarity Map

(2) Hierarchical registration – reliable points first

HAMMER

To minimize the effect of local minima

Few driving voxels

Smooth approximation of the energy function

Many driving voxels

Complete energy function

Voxels with distinct attribute vectors.Roots of sulci

Crowns of gyriAll boundary voxels

Beginning of registration End of registration

(2) Hierarchical registration – reliable points first

HAMMER

158 subjects Average Template

158 brains we used to construct average brain

Model brain

3D renderings

A subject before warping and after warping

Model

HAMMER in labeling brain structures:

Subject

HAMMER

- Cross-sectional views

Model Subject

HAMMER

Inner cortical surface

Outer cortical surface

Model Subject

- Label cortical surface

Registration – HAMMER

Template

Xue, Shen, et al., “Simulating Deformations of MR Brain Images for Evaluation of Registration Algorithms”, Neuroimage, Vol. 33: 855-866, 2006.

Simulating brain deformations for validating registration methods

Simulated

Successful applications of HAMMER:

10+ large clinical research studies and clinical trials involving >8,000 MR brain images:

• One of the largest longitudinal studies of aging in the world to date,

(an 18-year annual follow-up of 150 elderly individuals)

• A relatively large schizophrenia imaging study (148 participants)

• A morphometric study of XXY children

• The largest imaging study of the effects of diabetes on the brain to date,

(650 patients imaged twice in a 8-year period)

• A large study of the effects of organolead-exposure on the brain

• A study of effect of sustained, heavy drinking on the brain

Improving: Learning Best Features for Registration

Best-scale moments:

Wu, Qi, Shen, “Learning Best Features for Deformable Registration of MR Brains”, MICCAI, 2005.

Criteria for selecting best-scale moments of each point:• Maximally different from those of its nearby points. (Distinctiveness)• Consistent across different samples. (Consistency)• Best scales, used to calculate best-scale features, should be smooth spatially. (Regularization)

Moments w.r.t. scales:

Improving: Learning Best Features for Registration

• Visual improvement:

Model Ours HAMMER’s

Results:

• Average registration error:

Wu, Qi, Shen, “Learning-Based Deformable Registration of MR Brain Images”, IEEE Trans. Med. Imaging, 25(9):1145-1157, 2006.

Wu, Qi, Shen, “Learning Best Features and Deformation Statistics for Hierarchical Registration of MR Brain Images”, IPMI 2007.

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Error 2mm

HAMMER

Improved method

0.66mm 0.95mm

Histogram of deformation estimation errors

Improving: Statistically-constrained HAMMER

Template

Statistical Model of

Deformations, using wavelet-

PCA

Registration

Subject

HAMMER

Normal brain deformation captured from 150 subjects

Xue, Shen, et al., “Statistical Representation of High-Dimensional Deformation Fields with Application to Statistically-Constrained 3D Warping”, Medical Image Analysis, 10:740-751, 2006.

Improving: Statistically-constrained HAMMER

Comparison of Histograms of Jacobian Determinants

0.0%

0.5%

1.0%

1.5%

2.0%

0 1 2 3 4

Jacobian Determinant

Per

cent

age

HAMMER

SMD+HAMMER

• More smooth deformations:Results:

• Detection on simulated atrophy:

HAMMER SMD+HAMMER

White Matter Lesion (WML) Segmentation

• WMLs are associated with cardiac and vascular disease, and may lead to different brain diseases, such as MS.

• Manual delineation

• Computer-assisted segmentation

WML Segmentation

- Fuzzy-connection- Multivariate Gaussian Model- Atlas based normal tissue distribution model- KNN based lesion detection

• Lao, Shen, et al "Computer-Assisted Segmentation of White Matter Lesions in 3D MR images Using Support Vector Machine", Academic Radiology, 15(3):300-313, March 2008.

T1

PD

• Image property: serious intensity overlap in WMLs

WML

FLAIR

T2

Our approach

Attribute Vector

• Attribute vector for each point v

},,,{,| 21 FLAIRPDTTmvttIvF mmm Neighborhood Ω (5x5x5mm)

T1T2PDFLAIR

• SVM To train a WML segmentation classifier.

• Adaboost To adaptively weight the training samples and improve the generalization of WML segmentation method.

Co-registration

Skull-stripping

Intensity normalization

Pre-processing

Manual Segmentation

Training SVM model via training sample and Adaboost

Training

Voxel-wise evaluation & segmentation

Testing

False positive elimination

Post-processing

Overview of Our Approach

Results

• Paired Spearman Correlation (SC)

Gold standard (rater 1)

Rater 2 Computer Mean+dev. of the lesion volume

Gold standard (rater 1) 1.0 0.95 0.79 1494+/-3416 mm3

Rater 2 0.95 1.0 0.74 2839+/-6192 mm3

Computer 0.79 0.74 1.0 1869+/-3400 mm3

Results – 45 Subjects

Double

• Coefficient of variation (CV)

Coefficient of Variation

Rater 1 189%

Rater 2 218%

Computer 182%

To investigate the variation of the lesion load’s distribution of the 35 evaluated subjects

Defined as CV=/.

Close

10 for training, and 35 for testing

• Lao, Shen, et al "Computer-Assisted Segmentation of White Matter Lesions in 3D MR images Using Support Vector Machine", Academic Radiology, 15(3):300-313, March 2008.

• Improve the robustness of multi-modality image registration (for T1/T2/PD/FLAIR) by using a novel quantitative and qualitative measurement for mutual information, where salient points will be considered more during the registration.

• Design region-adaptive classifiers, in order to allow each classifier for capturing relative simple WML intensity pattern in each region; we will also develop a WML atlas for guiding the WML segmentation.

Improvement in this project

• Lao, Shen, et al "Computer-Assisted Segmentation of White Matter Lesions in 3D MR images Using Support Vector Machine", Academic Radiology, 15(3):300-313, March 2008.

Conclusion

Further develop HAMMER registration and WML

segmentation algorithms improve their

robustness and performance

3D Slicer

Thank you!

http://bric.unc.edu/IDEAgroup/http://www.med.unc.edu/~dgshen/ IDE

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