> For the complete documentation index, see [llms.txt](https://qiicr.gitbook.io/quantitativereporting-guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://qiicr.gitbook.io/quantitativereporting-guide/master.md).

# Introduction

![](/files/-L-1m0u9OKQxEXZrRi53)

## Overview

`QuantitativeReporting` is an [3D Slicer](http://slicer.org) extension to support segmentation-based measurements with DICOM-based import and export of the results. The extension comes with a variety of plugins for loading DICOM Segmentations, Parametric Maps and Structured reports into Slicer.

### Capabilities of `QuantitativeReporting` Include:

* Loading of DICOM image series (CT, MRI)
* Interactive image annotation using automated segmentation tools of 3D Slicer
* Automatic calculation of segmentation-based measurements statistics
* Saving of the segmentation-based measurements as a linked collection of DICOM objects: 
  * DICOM Structured Report [TID 1500](http://dicom.nema.org/medical/dicom/current/output/chtml/part16/chapter_A.html#sect_TID_1500) 
  * DICOM Segmentation 
* Loading and display of the volumetric measurements from SR+SEG

![](/files/-L-1m0uAj-XP8HrNBfTY)

#### Support

Please feel free to contact us for questions, feedback, suggestions, bugs, or you can create issues in the issue tracker: <https://github.com/QIICR/QuantitativeReporting/issues>

* [Andrey Fedorov](https://github.com/fedorov) <fedorov@bwh.harvard.edu>
* [Christian Herz](https://github.com/che85) <cherz@bwh.harvard.edu>

#### Acknowledgments

This work is supported in part the National Institutes of Health, National Cancer Institute, Informatics Technology for Cancer Research (ITCR) program, grant Quantitative Image Informatics for Cancer Research (QIICR) (U24 CA180918, PIs Kikinis and Fedorov).

#### References

1. [Informatics Technology for Cancer Research (ITCR)](http://itcr.nci.nih.gov/)
2. [Quantitative Imaging Network (QIN)](http://imaging.cancer.gov/programsandresources/specializedinitiatives/qin)
3. Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, et al. 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 \[Internet]. 2016;4:e2057. Available from: <http://dx.doi.org/10.7717/peerj.2057>
4. Sample data: [TCIA QIN-HEADNECK](https://wiki.cancerimagingarchive.net/display/Public/QIN-HEADNECK) collection


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