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

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Extension: [[Documentation/{{documentation/version}}/Extensions/dcmqi|dcmqi]]<br>
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Acknowledgments:
 
Acknowledgments:
This work was supported by the Quantitative Image Informatics project via the NIH-National Cancer Institute Grant U24 CA180918.<br>
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This work was supported by the [http://qiicr.org Quantitative Image Informatics for Cancer Research (QIICR)] project via the NIH-National Cancer Institute Grant U24 CA180918.<br>
 
Author: Andrey Fedorov ({{collaborator|name|spl}})<br>
 
Author: Andrey Fedorov ({{collaborator|name|spl}})<br>
 
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[[File:dcmqi-logo.png|x200px]]
 
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This extension contains the [https://github.com/dcmqi DICOM for Quantitative Imaging (dcmqi)] library that provides tools and API for conversions of the quantitative image analysis results (segmentations, measurements, parametric maps) into DICOM format and back.
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This extension contains the [https://github.com/QIICR/dcmqi#readme DICOM for Quantitative Imaging (dcmqi)] library that provides tools and API for conversions of the quantitative image analysis results (segmentations, measurements, parametric maps) into DICOM format and back.
 
   
 
   
 
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* [http://qiicr.org Quantitative Image Informatics for Cancer Research (QIICR)]
 
* [http://qiicr.org Quantitative Image Informatics for Cancer Research (QIICR)]
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* Herz C, Fillion-Robin JC, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. (2017) dcmqi: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer Research (in press) [[https://www.dropbox.com/s/qbj0n8an30upmk7/Herz2017-CancerResearch.pdf?raw=1 PDF]]
 
* 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
 
* 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
  

Latest revision as of 09:25, 1 September 2017

Home < Documentation < Nightly < Extensions < DCMQI

For the stable Slicer documentation, visit the 4.10 page.

Introduction and Acknowledgements

Extension: dcmqi
Acknowledgments: This work was supported by the Quantitative Image Informatics for Cancer Research (QIICR) project via the NIH-National Cancer Institute Grant U24 CA180918.
Author: Andrey Fedorov (SPL)
Contributor1: Christian Herz (SPL)
Contributor2: Jean-Christophe Fillion-Robin (Kitware)
Contact: Andrey Fedorov,

Quantitative Image Informatics for Cancer Research  
Surgical Planning Laboratory (SPL)  
Kitware, Inc.  

Extension Description

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This extension contains the DICOM for Quantitative Imaging (dcmqi) library that provides tools and API for conversions of the quantitative image analysis results (segmentations, measurements, parametric maps) into DICOM format and back.

Use Cases

Tutorials

Usage overview and documentation for the dcmqi library are available at https://github.com/QIICR/dcmqi#introduction

Panels and their use

The dcmqi extension doesn't provide a GUI, it's intended to be used at the library level by other modules.

Similar Extensions

References

  • Quantitative Image Informatics for Cancer Research (QIICR)
  • Herz C, Fillion-Robin JC, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. (2017) dcmqi: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer Research (in press) [PDF]
  • 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

Information for Developers