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Core 1b – Engineering Data Management

Explore the architecture, tools, and reporting mechanisms for engineering data management. Learn about end-user and computational platforms, data management techniques, and software engineering best practices.

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Core 1b – Engineering Data Management

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  1. Core 1b – EngineeringData Management Stephen R. Aylward Kitware, Inc.

  2. Core 1b – Engineering5 Aims / 5 Platforms 4 1 Architecture – tools, operating paradigms, reporting mechanisms, integration points End-user platform – interactive methods and information visualization for longitudinal analysis, exploratory data analysis, and translational research Computational platform – stream processing, cloud computing, statistical analysis, informatics, machine learning Data management – non-imaging and derived data, DICOM and cloud services Software engineering and software quality – navigable timeline for revision control, build, test, documentation and release 3 2 5

  3. Data Management • Key, bi-directional link between clinicians and researchers • Foundational to transitioning algorithms from research to clinical practice • Essential to quantitative comparison of algorithms • January 2011, AHM • 11 projects emphasizing data management • Registration Case Library • MRML XML Schema • XNAT for Non-Image Data • DICOM-RT

  4. Data Management • Driving Biological Problems • Data Management challenges: Transfer, Archive, Clinical Interpretation, and Reporting (DICOM) • Algorithms Team • Aim 1: Statistical models of anatomy and pathology • Aim 4: Longitudinal and Time-Series Analysis • Milestones: Validation, Optimization, Atlases, Priors

  5. Success = Challenge

  6. Data Management Technology VTK: InfoVis

  7. The Extensible Neuroimaging Archive Toolkit (XNAT) • Management and exploration of neuroimaging and related data. • Secure database backend • Rich web-based user interface • XNAT 1.4 • XNAT Desktop (XND) • What’s next: • XNAT for Non-Image Data • Slicer4: FetchMI

  8. MIDAS in-use • Insight Journal • NA-MIC: PubDB • RIRE Project • Optical Society of America: Interactive Scientific Publication • Give-a-scan: Patient-created lung CT database • What’s Next • Slicer Testing (BrainsFit Data: Uiowa, Kitware) • Server-Side Processing for Algorithm Validation • ITK A2D2: Score and Score++ (Kitware, Utah, BWH) • NIH R01: CoValic (Kitware, Utah) • NIH SBIR: High-Throughput Small Animal Imaging (NAMIC-Kit, Kitware, UNC) • Slicer4: FetchMI

  9. DCMTK 286,000 Lines 76 Person Years $4.2M (Ohloh) 3DSlicer V4 CTK Common Toolkit VTK, QT, DCMTK,DICOM Services (XIP) ITKv4 DICOM Objects and Networking GDCM, DCMTK, Validation

  10. Non-Image Data: MRML & Informatics vtkTable vtkSQLQuery vtkTable vtkSQLQuery vtkSQLDatabase vtkTable vtkSQLQuery vtkTable Database vtkSQLQuery Datasources Datastructures Visualizations and Algorithms Figures from Titan presentation at VisWeek 2008

  11. Data Management Technology VTK: InfoVis

  12. Core 1b – Engineering5 Aims / 5 Platforms 4 1 Architecture – tools, operating paradigms, reporting mechanisms, integration points End-user platform – interactive methods and information visualization for longitudinal analysis, exploratory data analysis, and translational research Computational platform – stream processing, cloud computing, statistical analysis, informatics, machine learning Data management – non-imaging and derived data, DICOM and cloud services Software engineering and software quality – navigable timeline for revision control, build, test, documentation and release 3 2 5

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