Dinggang Shen Development and Dissemination of Robust Brain MRI Measurement Tools ( 1R01EB ) Department of Radiology and BRIC UNC-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 Striping 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 e1e1 e2e2 (1.5 hours) Template  Tang et. al., RABBIT: Rapid Alignment of Brains by Building Intermediate Templates. Neuroimage, 47(4): , Oct Progress of HAMMER in 2009

Construct a statistical deformation model Estimate an intermediate deformation/template Refine the intermediate deformation field e1e1 e2e2 12~15 mins Subject Progress of HAMMER in 2009  Tang et. al., RABBIT: Rapid Alignment of Brains by Building Intermediate Templates. Neuroimage, 47(4): , Oct

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): , Feb 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 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): , March Neighborhood Ω (5x5x5mm) T1 T2 PD FLAIR

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 TrainingSegmentation 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 resultGround 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! IDEA