Core 1 & Core 3 Projects

1. Core 1 & Core 3 Projects

2. Existing projects: Core 1 & 3 Harvard and University of North Carolina Shapeanalysis of caudate Automatic Segmentation of corpus callosum based on Diffusion Fiber Tracking Model ITK SNAP: Level set semiautomatic segmentation tool Statistical analysis of DTI measures along white matter fibers [Participants: UNC: Isabelle Corouge, Martin Styner, Guido Gerig PNL: Sylvain Bouix, Marek Kubicki, James Levitt, Marc Niethammer, Martha Shenton]

3. Existing projects: Core 1 & 3 Harvard and Massachusetts Institute of Technology Diffusion measures along cingulum bundle fiber tracts Clustering of specific fiber tracts based on location & regions they connect Atlas of human brain white matter fiber bundles using automatic population based clustering FA/Trace measures of corpus callosum and anterior commissure [Participants: MIT: Lauren O’Donnell, CF Westin, Scott Hoge, Raul San Jose, Eric Grimson PNL: Marc Niethammer, Sylvain Bouix, Marek Kubicki, Mark Dreusicke, Martha Shenton] Brain tissue classification and subparcellation of brain structures [Participants: MIT: Kilian Pohl, Sandy Wells, Eric Grimson PNL: Sylvain Bouix, Motaki Nakamura, Min-Seong Koo, Martha Shenton]

4. Existing projects: Core 1 & 3 Harvard and Georgia Tech Semiautomatic segmentation and parcellation of basal ganglia [Participants: GTech: Ramsey Al-Hakim, Delphine Nain, Allen Tannenbaum PNL: Sylvain Bouix, James Levitt, Marc Niethammer, Martha Shenton]

5. Existing projects: Core 1 & 3 UCI and Georgia Tech Semiautomatic segmentation and parcellation of cortical and subcortical areas [Participants: GTech: Ramsey Al-Hakim, Delphine Nain, Allen Tannenbaum UCI: Jim Fallon, Vid Petrovic, Martina Panzenboeck]

6. Existing projects: Core 1 & 3 UCI and UNC Automated DTI tractography and atlas development

7. Existing projects: Core 1 & 3 Harvard and Utah New anisotropic measures for white matter diffusion [Participants: Utah: Tom Fletcher, Ross Whitaker PNL: Sylvain Bouix, Marek Kubicki, Martha Shenton]

8. Quantitative Fiber Tract AnalysisUCI and UNC • For clinical studies • UNC: neonatal studies in autism, SZ • For neuroanatomy and connectivity exploration • NAMIC collaboration with UC Irvine (Jim Fallon) • NAMIC collaboration with Shenton/Marek • UNC: Krabbe’s disease • UNC: Neonatal & Autism Studies • UNC: Healthy Aging Study [Fallon]

9. Rule-Based Brain Segmentation • We are developing common tools needed for rule-based semi-automatic segmentation algorithms • 3 Prototype programs have been created to segment different brain structures based on neurological rules and minimal user input

10. Common tool: “Thumb” Extraction: UCI and GaTech • Extraction of “thumbs” using an intensity-based energy minimized using Fast Marching methods • Applications to rule-based algorithms • Currently being ported from Matlab to VTK “Thumb” John Melonakos (GaTech), Jim Fallon (UCI)

11. Example: Segmentation of Putamen: UCI/Ga Tech MRI image of striatum showing the putamen. Gradient of the image showing edge information. • The user specifies several points on the border of the Putamen on each slice. • The algorithm finds the lowest cost outline of the structure based on edge information in the image. • A 3D model is created for analysis Shawn Lankton (GaTech), Jim Fallon (UCI)

12. Example: Rule Based Segmentation of the Striatum in Slicer: Harvard/GaTech • Begin with manually segmented label of total striatum • Manually input most superior/dorsal point on putamen and anterior commissure; striatum is delineated automatically based on rules of Dr. James Levitt. Most superior/dorsal point on putamen Anterior commissure Ramsey Al-Hakim (GaTech), James Levitt (SPL)

13. Pre Caudate Post Caudate Post Putamen Nucleus Accumbens Pre Putamen Example: Rule Based Segmentation of the Striatum in Slicer: Harvard/GaTech Anterior/Superior View of Delineated Striatum Automatically marked label (blue lines input by user to designate superior/dorsal point on putamen) Ramsey Al-Hakim (GaTech), James Levitt (SPL)