2012 Progress Report HIGHLIGHTS

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2 Highlights

The scope of NA-MIC activities includes advanced medical image analysis research combined with leading edge software processes and computational platforms. To reflect these activities, the NA-MIC Computer Science Core efforts are organized around two teams: Algorithms and Engineering. Their joint output is the NA-MIC Kit which embodies a comprehensive set of analysis techniques in a well architected, documented, and widely used platform as described in the following paragraphs.

Algorithms. The NA-MIC Computer Science Algorithm effort responds to the challenges of the DBPs to expand the horizons of medical image analysis. As a result, the Algorithm activities are typically highly experimental, creating new approaches that are rapidly prototyped, tested, and improved.

Engineering. The NA-MIC Computer Science Engineering effort supports the needs of the Algorithms effort by creating integrated software platforms supporting research and eventual deployment of advanced technology. The Engineering team also develops and maintains processes used to build and sustain a large research community.

NA-MIC Kit. The NA-MIC Kit consists of an integrated set of interoperable free open source software (FOSS) packages; developed, supported and deployed using a collaborative, agile, high quality software process. The NA-MIC Kit has been constructed as a layered architecture to provide a spectrum of capabilities, ranging from compute-intensive algorithms to easy-to-use applications. Hence users and developers can choose to engage the NA-MIC Kit at a variety of levels, including developing extensions which can be readily deployed to the broader biomedical imaging community.

In the following subsections we highlight the accomplishments from this reporting period for algorithms, engineering, and NA-MIC kit.

2.1 Algorithms

The Algorithms team develops computational methods supporting patient-specific analysis of medical images. This requires analysis of images that vary significantly from one patient to another, or from one time point to another, which present distinct challenges to existing state-of-art medical image analysis algorithms. These technical challenges were addressed using four computational approaches: (1) Statistical models of anatomy and pathology; (2) Geometric correspondence; (3) User interactive tools for segmentation; and (4) Longitudinal and time-series analysis. Highlights of these efforts are described in the following.

Statistical models of anatomy and pathology. A great deal of progress has been made by using modeling approaches that systematically capture the statistics of a problem domain from a collection of examples and then use these statistics to interpret novel images. Some of the approaches include:

  • Non-Parametric Priors for Segmentation
  • Fast nearest-neighor lookup in large image databases
  • Atlases and Registration for DTI Processing

Geometric correspondence. v

  • Stochastic Point Set Registration
  • Automatic Correspondences For Shape Ensembles

User interactive tools for segmentation. v

  • Controlled Based Interactive Segmentation
  • Globally Optimal Segmentation
  • Patient-Specific Segmentation Framework for Longitudinal MR Images of Traumatic Brain Injury

Longitudinal and time-series analysis. v

  • Connectivity Changes in Disease
  • Modeling Pathology Evolution
  • Longitudinal Analysis of DTI Change Trajectories
  • Analysis of Longitudinal Shape Variability via Subject Specific Growth Modeling
  • Longitudinal and Time Series Analysis

2.2 Engineering


  • Slicer 4.0 Release
    • modern, stable platform
  • Extending Slicer
    • Python has been adopted as the preferred scripting language,
    • Slicer Extension Manager is now the "Slicer Catalog.
  • New Features
    • Multivolume analysis
    • Interactive methods
    • Modern Cross-Platform Design Patterns:
    • Efficiency and Robustness
    • Expectation Maximization (EM) Segmenter;

2.3 NA-MIC Kit

The NA-MIC Kit is designed to accelerate the pace of research and facilitate clinical evaluation. Along these lines, the past year realized significant milestones towards the creation of a stable research platform, supporting the ability to easily extend and disseminate novel additions, all in the context of a world-wide, broad research community. Beyond the major highlights related to Slicer 4.0 application platform described in the previous section, the following are a few of the highlights of the past year.

  • CMake
  • CDash Package Manager
  • Data (XNAT and DICOM including DICOM lollipops, DCMTK)
  • Community (CTK, BRAINSFit,
  • Plans: Slicer 4.1 including charting and Slicer Catalog.