2012 Progress Report HIGHLIGHTS
THIS IS A PLACEHOLDER -- NOT YET COMPLETED
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.
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 are based on nonparametric, probabilistic models for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject.
- Fast Nearest-Neighor Lookup in Large Image Databases
- Atlases and Registration for DTI Processing
Geometric correspondence. Establishing anatomical correspondences between pairs of patients, groups of patients, patients and templates, and individual patients over time is important for automatic and user-assisted image analysis. The ability to establish geometric correspondences, with and without expert guidance, in challenging clinical circumstances is essential for the DBPs. Progress in two areas was realized this year.
- Stochastic Point Set Registration
- Automatic Correspondences For Shape Ensembles
User interactive tools for segmentation. The work performed in the past year addresses important aspects of user-interactive segmentation. The patient-specific analysis required by the DBPs has presented images of patients with pathologies and/or injuries that sometimes defy automated approaches. We have focused our research on three principal areas.
- Controlled Based Interactive Segmentation
- Globally Optimal Segmentation
- Patient-Specific Segmentation Framework for Longitudinal MR Images of Traumatic Brain Injury
Longitudinal and time-series analysis. An important component of patient-specific data analysis is the ability to analyze multiple images from the same patient over time, as a disease or injury progresses or responds to treatment, or to assess neurodevelopment or neurodegeneration. Longitudinal image analysis is important for all four DBPs in this project; we have focused in the past year in the areas described below.
- 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
- 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.
- CDash Package Manager
- Data (XNAT and DICOM including DICOM lollipops, DCMTK)
- Community (CTK, BRAINSFit,
- Plans: Slicer 4.1 including charting and Slicer Catalog.