Documentation/Nightly/Extensions/QuantitativeReporting

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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

• Informatics Technology for Cancer Research (ITCR)
• 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
• Sample data: TCIA QIN-HEADNECK collection

Information for Developers

• Source code of the extension: https://github.com/fedorov/Reporting
• Running EncodeSEG from Editor: https://github.com/Slicer/Slicer/blob/master/Modules/Scripted/EditorLib/EditUtil.py#L364-L505