2017 Winter Project Week/QuantitativeReporting

From NAMIC Wiki
Revision as of 15:22, 13 January 2017 by Ch399 (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search
Home < 2017 Winter Project Week < QuantitativeReporting

Key Investigators

  • Christian Herz, BWH
  • Andrey Fedorov, BWH
  • Andras Lasso, Queen's
  • Csaba Pinter, Queen's

Project Description

An extension of 3D Slicer to support segmentation-based measurements with DICOM-based import and export of the results.

Objective Approach and Plan Progress and Next Steps
  • utilizes dcmqi library for reading/writing of DICOM SEG/SR
  • demonstrate Quantitative Reporting extension
  • improve documentation
  • discuss next steps and specific open issues
  • get rid of hacks that hide SegmentEditor components individually by iterating over GUI components
  • add functionality for continuing a saved report
  • communication of measurements tables and integrated support into Slicer Table node (CSV/JSON/other approaches - see https://github.com/QIICR/QuantitativeReporting/issues/105)
  • integration of measurement quantities/units into Slicer for volume nodes
  • discussions with Csaba Pinter about SegmentEditor and making UI components easier accessible to developers by providing methods
  • implementation for continuing a partially completed measurement report
    • works with Slicer created measurement report
    • problems:
      • recalculation of measurements might not be possible (no information about algorithms that have been used for that)
      • Quantitative Reporting for now only uses SegmentStatistics module for computing measurements
    • TODO: reference to original measurement report for being able to trace modifications


Background and References

  • dcmqi home page: http://github.com/qiicr/dcmqi
  • Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, Onken M, Riesmeier J, Pieper S, Kikinis R, Buatti J, Beichel RR. (2016) DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ 4:e2057 https://doi.org/10.7717/peerj.2057