1. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Core 1 & Core 3 Projects

2. National Alliance for Medical Image Computing http://na-mic.org Existing projects: Core 1 & 3 Harvard and University of North Carolina Shape analysis 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. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org Existing projects: Core 1 & 3 UCI and UNC Automated DTI tractography and atlas development

7. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org Quantitative Fiber Tract Analysis UCI 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. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org MRI image of striatum showing the putamen.Gradient of the image showing edge information. Example: Segmentation of Putamen: UCI/Ga Tech 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. National Alliance for Medical Image Computing http://na-mic.org 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 putamenAnterior commissure Ramsey Al-Hakim (GaTech), James Levitt (SPL)

13. National Alliance for Medical Image Computing http://na-mic.org http://www.na-mic.org/Wiki/index.php/Progress_Report:DTI_Path_of_interest_analysis Example: Path-of-interest analysis (Dartmouth/MGH/Isomics) Path-of-interest reconstruction Dartmouth DTI data Slicer visualization Saykin (Dartmouth), West (Dartmouth), Snyder (MGH), Tuch (MGH), Pieper (Isomics)

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