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

- Guorong Wu (postdoc)

- Minjeong Kim (postdoc)

• 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.

Progress of HAMMER in 2009

• Successfully implemented HAMMER in ITK.

(Over 2,000 lines of code)• Integrated HAMMER into Slicer3• Verified and tested its performance in Slicer3

Input Subject

AC/PC

Skull Stripin

g

Segmentation

Progress of HAMMER in 2009

Template

Subject

Registration result

Typical Registration Result of HAMMER in Slicer3

Average of 18 aligned images

RABBIT: To speed up our HAMMER registration algorithm (1.5 hours)

12~15 minutes

Subject

e1

e2

(1.5 hours)

Template

Tang et. al., RABBIT: Rapid Alignment of Brains by Building Intermediate Templates. Neuroimage, 47(4):1277-87, Oct 1 2009.

Progress of HAMMER in 2009

Construct a statistical deformation model

Estimate an intermediate deformation/template

Refine the intermediate deformation field

e1

e2

12~15 minsSubject

Progress of HAMMER in 2009

Tang et. al., RABBIT: Rapid Alignment of Brains by Building Intermediate Templates. Neuroimage, 47(4):1277-87, Oct 1 2009.

Progress of HAMMER in 2009

Wu et. al., TPS-HAMMER: Improving HAMMER Registration Algorithm by Soft Correspondence Matching and Thin-Plate Splines Based Deformation Interpolation. Neuroimage, 49(3):2225-2233, Feb 2010.

TPS-HAMMER:

• Use soft correspondence detection to robustly establish correspondences for the driving voxels

• Use Thin Plate Splines (TPS) to effectively interpolate deformation fields, based on those estimated at the driving voxels

Work Plan of HAMMER in 2010

• Further improve HAMMER in Slicer3– Implement RABBIT to speedup the registration

– Implement TPS-HAMMER in ITK

– Implement intensity-HAMMER in ITK

• Serve HAMMER user community– To provide training and tutorial

– To provide technical support

– To develop user-friendly interface to the end user

WML Segmentation

• Attribute vector for each point v

},,,{,| 21 FLAIRPDTTmvttIvF mmm

• SVM To train a WML segmentation classifier.

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

• 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.

Neighborhood Ω (5x5x5mm)T1T2PDFLAIR

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

Progress in 2009

• We have implemented all WML segmentation components in ITK

Progress in 2009

• Have incorporated it into Slicer3

Developer Tools >> White Matter Lesion Segmentation

Progress in 2009

Training Segmentation

• User interface of WML segmentation in Slicer3

• Input: T1, T2, PD, FLAIR images and lesion ROI of n training subjects• Output: SVM model

• Input: T1, T2, PD, FLAIR images of test subject(s) and trained SVM model• Output: segmented lesion ROI

• A typical segmentation result

Progress in 2009

Our result Ground truthFLAIR

• Further development of WML segmentation algorithm

– Improve the robustness of multi-modality image registration (for T1/T2/PD/FLAIR) by using a novel quantitative and qualitative measurement for mutual information

– Design region-adaptive classifiers, in order to allow each classifier for capturing relative simple WML intensity pattern in each region

– Develop a WML atlas for guiding the WML segmentation

• Upgrade of WML lesion segmentation module in Slicer3

Plan of 2010

Conclusion

Further develop HAMMER registration and WML

segmentation algorithms improve their

robustness and performance

Thank you!

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

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