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Robust Brain MRI Measurement Tools Development and Dissemination

Develop and disseminate robust MRI measurement tools for brain imaging analysis, enhancing registration and lesion segmentation algorithms. Progress includes successful implementation into Slicer3 and integration with ITK for improved performance. The HAMMER algorithm, RABBIT for alignment, TPS-HAMMER for deformation, and intensity-HAMMER have shown advancements. Future goals involve further improving these tools, training, technical support, and user-friendly interfaces. For white matter lesion segmentation, attributes vectors, SVM training, Adaboost weighting, and WML atlas development are key areas of focus. Enhancements to multi-modality image registration and region-adaptive classifiers will contribute to improved WML segmentation outcomes in Slicer3.

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Robust Brain MRI Measurement Tools Development and Dissemination

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  1. Image Display, Enhancement, and Analysis IDEA Development and Dissemination of Robust Brain MRI Measurement Tools (1R01EB006733) Dinggang Shen • Department of Radiology and BRIC • UNC-Chapel Hill

  2. UNC-Chapel Hill - Dinggang Shen - Guorong Wu (postdoc) - Minjeong Kim (postdoc) GE - Jim Miller - Xiaodong Tao Team

  3. Goal of this project • To further developHAMMER 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 the3D Slicer.

  4. 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 Striping Segmentation

  5. Progress of HAMMER in 2009 Typical Registration Result of HAMMER in Slicer3 Template Average of 18 aligned images Subject Registration result

  6. Progress of HAMMER in 2009 RABBIT: To speed up our HAMMER registration algorithm (1.5 hours) e2 12~15 minutes Template e1 (1.5 hours) Subject • Tang et. al., RABBIT: Rapid Alignment of Brains by Building Intermediate Templates. Neuroimage, 47(4):1277-87, Oct 1 2009.

  7. Progress of HAMMER in 2009 Construct a statistical deformation model e2 e1 Estimate an intermediate deformation/template 12~15 mins Refine the intermediate deformation field Subject • Tang et. al., RABBIT: Rapid Alignment of Brains by Building Intermediate Templates. Neuroimage, 47(4):1277-87, Oct 1 2009.

  8. Progress of HAMMER in 2009 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 • 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.

  9. 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

  10. WML Segmentation • Attribute vector for each point v FLAIR PD T2 T1 Neighborhood Ω(5x5x5mm) • 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.

  11. Progress in 2009 • We have implemented all WML segmentation components in ITK Manual Segmentation Co-registration Skull-stripping Training SVM model via training sample and Adaboost Intensity normalization Pre-processing Training Voxel-wise evaluation & segmentation False positive elimination Testing Post-processing

  12. Progress in 2009 • Have incorporated it into Slicer3 • Developer Tools >> White Matter Lesion Segmentation

  13. Progress in 2009 • User interface of WML segmentation in Slicer3 Training Segmentation • 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

  14. Progress in 2009 • A typical segmentation result FLAIR Our result Ground truth

  15. Plan of 2010 • 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

  16. Conclusion Further developHAMMER registration and WML segmentation algorithmsimprove their robustness and performance

  17. Image Display, Enhancement, and Analysis IDEA Thank you! http://bric.unc.edu/IDEAgroup/ http://www.med.unc.edu/~dgshen/

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