2007 Annual Scientific Report

Back to 2007_Progress_Report

For reference:

• 2006 Annual Scientific Report, 2006 Followup Questions and Answers.
• 2005 Annual Scientific Report

2. Four Main Themes

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

• Core 1 Algorithms-Ross Whitaker PI
• Core 2 Engineering-Will Schroeder PI
• Core 3 DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
• Core 4 Service-Will Schroeder PI
• Core 5 Training-Randy Gollub PI
• Core 6 Dissemination-Tina Kapur Co-PI; Steve Pieper Co-PI
• Core 7 Leadership-Ron Kikinis

2.1 Diffusion Image Analysis Theme (Marek Kubicki, Guido Gerig)

Key Investigators

• BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Katharina Quintus, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann, Doug Markant
• Harvard/MGH: Bruce Fischl, Denis Jen, David Kennedy
• MIT: Lauren O'Donnell
• UCI: James Fallon, Martina Panzenboeck
• UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
• Utah: Tom Fletcher, Ross Whitaker, Saurav Basu
• Georgia Tech: Eric Pichon, John Melonakos, Xavier LeFaucheur, Allen Tannenbaum
• Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
• Isomics: Steve Pieper

Additional Information

For details of each of the projects in this theme, please see NA-MIC Projects on Diffusion Image Analysis.

2.2 Structural Analysis Theme (Allen Tannenbaum, Martin Styner)

Progress

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g. in that segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

Segmentation

• Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

• Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

Shape Analysis

• Wavelet based Shape analysis: +++Allen, can you edit/complete this+++ A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity.
• Curvature based population correspondence: with We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).
• Shape Analysis Toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attribured surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.
• Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application to the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

Key Investigators

• MIT: Kilian Pohl, Sandy Wells, Eric Grimson
• UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
• Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
• GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
• Steve Pieper, Bill Lorensen, Luis Ibanez, Karthik Krishnan, Michael J. Pan, Jagadeeswaran Rajendiran, Jim Miller, Karthik Krishnan, Luis Ibanez
• Harvard PNL: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
• Dartmouth: Andrew Saykin
• UCI: James Fallon

Additional Information

For details of each of the projects in this theme, please see NA-MIC Projects on Structural Image Analysis.

2.3 Functional MRI Analysis Theme (Polina Golland, Andy Saykin)

Progress

During this year, the focus of the algorithms and the engineering cores has been on the structural and DTI analysis. While we continued to expand the methods and the infrastructure in NAMIC-kit to support fMRI analysis, as well as using the analysis tools to perform clinical studies, the emphasis of the work this year has been on integrating the fMRI analysis with other modalities and supporting other modalities.

Clinical Studies

We would like to highlight several clinical studies within NAMIC that focused on fMRI data and its relationship with other imaging modalities:

• Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated using partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task).
  Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

• Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results).
  Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

• Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities.
  Publications: West JD, Saykin AJ, Roth RM, Flashman LA, Koven N, Pendergrass JC, Arfanakis K. Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers. 13th Annual Meeting of the Organization for Human Brain Mapping, Chicago, IL, USA, June, 2007. Journal paper in preparation.

Methods

During this year, we continued methodological development along two directions:

• Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

• Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

Key Investigators

• MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
• Harvard: Martha Shenton, Marek Kubicki
• Dartmouth: Andy Saykin
• UCI Irvine: Jessica Turner, Stephen Potkin
• Toronto: Jim Kennedy

2.4 NA-MIC Kit Theme (Will Schroeder)

Progress

The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

1. Unrestrictive License. Users of the Kit are free to distribute their derived works under any license suitable to their needs.
2. Cross Platform. This software set can be adapted to the best available price-performance computer systems for any particular use.
3. Extensible Application Framework. New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
4. Quality Software Process. Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
5. Sustainable Community. Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

Slicer3

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above Extensible Application Framework. The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.

• Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
• The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
• A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
• Support for editing and marking data including support for fudicials, paint and draw editors.
• The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

EM Segment

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the EM Segment module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm (Pohl et al.). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

Quality Software Process

Building on last year's success with the KDE community, the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

Key Investigators

• GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
• Isomics: Steve Pieper, Alex Yarmarkovich
• Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
• UCLA: Mike Pan, Jagadeeswaran Rajendiran
• UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
• Harvard: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
• MIT: Lauren O'Donnell, Kilian Pohl

Additional Information

For details of each of the projects in this theme, please see NA-MIC Kit Projects.

3. Highlights (Will Schroeder)

The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

3.1 Advanced Algorithms

Core 1 continues to lead the biomedical community in DTI and shape analysis.

• NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
• The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
• The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
• The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

3.2 Technology Deployment Platform: Slicer3

Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.

• One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

• Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

• To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

3.3 Outreach and Technology Transfer

Cores 4-5 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.

• NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. This year's first Project Week was held in January and hosted by the University of Utah. It saw everal significant accomplishments including the first beta release of the next generation Slicer3 computing platform. The second Project Week is scheduled for June in Boston, MA.
• Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in IEEE Software. All three DTI papers presented at MICCAI last year were NAMIC associated.
• Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

4. Impact and Value to Biocomputing (Jim Miller)

In NA-MIC's third year, it is evident that NA-MIC is developing a culture, environment, and resources to foster and incite collaborative research in medical image analysis that draws together mathematicians, computer scientists, software engineers, and clinical researchers. These artefacts of NA-MIC impact how NA-MIC operates, make NA-MIC a fulcrum for NIH funded research, and draws new collaborators from across the country and around the world to NA-MIC.

4.1 Impact within the Center

Within the center, the NA-MIC organization, NA-MIC processes, and the NA-MIC calendar has permeated the research. The organization is nimble, forming ad hoc distributed teams within and between cores to address specific biocomputing tasks. Information is shared freely on the NA-MIC Wiki, on the weekly Engineering telephone conferences, and in the NA-MIC Subversion source code repository. The software engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets facilitate a cross platform software environment for medical image analysis that be easily built, tested, and distributed to end-users. Core 2 has provided a platform, Slicer 3, that allows Core 1 to easily integrate new technology and deliver this technology in an end user application to Core 3. Core 1 has developed a host of techniques to apply to structural and diffusion analysis which are under evaluation by Core 3. Major NA-MIC events, such as the annual All Hands Meeting, the Summer Project Week, the Spring Algorithms meeting, and Engineering Teleconferences are avidly attended by NA-MIC researchers as opportunities to foster collaborations.

4.2 Impact within NIH Funded Research

Within NIH funded research, NA-MIC continues to forge relationships with other large NIH funded projects such as BIRN, caBIG, NAC, and IGT. Here, we are sharing the NA-MIC culture, engineering practices, and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts BIRN wikis. caBIG lists the 3D Slicer among the applications available on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC infrastructure and are involved in the development of the 3D Slicer. BIRN recently held an event modeled after the NA-MIC Project Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have been funded under PAR-05-063 to collaborate with NA-MIC: Automated FE Mesh Development and Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI. Five additional applications to collaborate with NA-MIC via the NCBC collaborative grant mechanism are under consideration. Additional grant applications submitted under other calls are planning to use and extend the NA-MIC tools.

4.3 National and International Impact

NA-MIC events and tools garner national and international interest. There were nearly 100 participants at the NA-MIC All Hands Meeting in January 2007, with many of these participants from outside of NA-MIC. Several researchers from outside the NA-MIC community have attended the Summer Project Weeks and the Winter Project Half-Weeks to gain access to the NA-MIC tools and people. These external researchers are contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software engineering tools of CMake, Dart 2 and CTest are used by many open source projects and commercial applications. For example, the K Desktop Environment (KDE) for Linux and Unix workstations uses CMake and Dart. KDE is one of the largest open source projects in the world. Many open source projects and commercial products are benefiting from the NA-MIC related contributions to ITK and VTK. Finally, Slicer 3 is being used as an image analysis platform in several fields outside of medical image analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the Workshop on Open Science at the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2006 conference. The proceedings of the workshop are published on the electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced.

5.NA-MIC Timeline (Ross Whitaker)

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

2007 Scientific Report Timeline

6. EAB Report

The NA-MIC External Advisory Board (EAB), chaired by Prof. Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center (Appendix 2).

Logistics

Schedule and process for preparation of this report

• March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
• April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
• April 13 - update projects list using last year's NA-MIC_Collaborations and projects pursued at the half week in SLC. Remind investigators to update individual pages. (Tina)
• April 23- complete project description pages in updated list: NA-MIC_Collaborations (all project owners).
• April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
• May 3 - submit wiki report to NA-MIC editor, Ann (Tina)
• May 17 - submit Edited report to Rachana (Ann)
• May 31 - ship final package to NIH (Rachana)

Guidelines from NIH Program Officer

The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

1. Introductory page describing the new grouping of NAMIC project themes.
2. A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
3. A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
4. A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
5. A discussion of NAMIC’s impact and value to the biocomputing community this year.