Difference between revisions of "Documentation/Nightly/Extensions/QuantitativeReporting"

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* [http://imaging.cancer.gov/programsandresources/specializedinitiatives/qin Quantitative Imaging Network (QIN)]
 
* [http://imaging.cancer.gov/programsandresources/specializedinitiatives/qin Quantitative Imaging Network (QIN)]
 
* Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, Onken M, Riesmeier J, Pieper S, Kikinis R, Buatti J, Beichel RR. (2015) DICOM for quantitative imaging biomarker development: A standards based approach to sharing of clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ PrePrints 3:e1921 https://doi.org/10.7287/peerj.preprints.1541v1
 
* Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, Onken M, Riesmeier J, Pieper S, Kikinis R, Buatti J, Beichel RR. (2015) DICOM for quantitative imaging biomarker development: A standards based approach to sharing of clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ PrePrints 3:e1921 https://doi.org/10.7287/peerj.preprints.1541v1
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* Sample data: [https://wiki.cancerimagingarchive.net/display/Public/QIN-HEADNECK TCIA QIN-HEADNECK collection]
  
 
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Revision as of 14:17, 5 January 2016

Home < Documentation < Nightly < Extensions < QuantitativeReporting


For the latest Slicer documentation, visit the read-the-docs.


Template:Documentation/Nightly/extension-header

Introduction and Acknowledgements

Extension: Reporting
Acknowledgments: This work is done as part of the Informatics Technology for Cancer Research (ITCR) and Quantitative Imaging Network (QIN) initiatives of the National Cancer Institute, and is funded by the National Institutes of Health, National Cancer Institute through the grants U24 CA180918 (PIs Kikinis & Fedorov) and U01 CA151261 (PI Fiona Fennessy).
Author: Andrey Fedorov (SPL), Nicole Aucoin (SPL), Steve Pieper (SPL)
Contact: Andrey Fedorov, fedorov at bwh dot harvard dot edu
License: Slicer License

Surgical Planning Laboratory (SPL)  
Isomics, Inc.  
National Alliance for Medical Image Computing (NA-MIC)  
Quantitative Image Informatics for Cancer Research  


Modules

  • EncodeSEG: converter of segmentations into DICOM Segmentation image format
  • SEG2NRRD: converter of segmentations from DICOM Segmentation image format

References

Information for Developers