# DICOM data wrangling

In this part of the tutorial we will learn how to work with a DICOM dataset spanning different TCIA collections and containing various types of DICOM objects. 

### Hands-on demo notes

* TCIA as a use case
  * [LIDC-IDRI](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI) (annotations in XML)
  * [TCGA-GBM](https://wiki.cancerimagingarchive.net/display/Public/TCGA-GBM) ([annotations in NIfTI](https://wiki.cancerimagingarchive.net/display/DOI/Segmentation+Labels+and+Radiomic+Features+for+the+Pre-operative+Scans+of+the+TCGA-GBM+collection))
  * [TCGA-LGG](https://wiki.cancerimagingarchive.net/display/Public/TCGA-LGG) ([annotations in NIfTI](https://wiki.cancerimagingarchive.net/display/DOI/Segmentation+Labels+and+Radiomic+Features+for+the+Pre-operative+Scans+of+the+TCGA-LGG+collection))
  * [QIN-HEADNECK](https://wiki.cancerimagingarchive.net/display/Public/QIN-HEADNECK)
  * QIN-PROSTATE-Repeatability (not yet released)
* discuss the example dataset used in the demo
* steps for handling DICOM data:
  * [dicomsort](https://github.com/pieper/dicomsort)
  * [dcm2niix](https://github.com/rordenlab/dcm2niix) for converting image series into volumes
  * [dcmqi](https://github.com/qiicr/dcmqi) for working with SEG and SR
  * [dcm2tables](https://github.com/QIICR/dcm2tables): conversion into tabular representation for working with metadata
    * "database" [visual schema](<https://app.quickdatabasediagrams.com/#/schema/_71V1H1AXEqqKWDnvx4VXw >)
  * Jupyter Notebook demonstration

This part will be covered in [this Jupyter Notebook](https://github.com/QIICR/dicom4miccai-handson/blob/master/notebooks/multicollection.ipynb).

### QIN-HEADNECK collection exploration&#x20;

See [this Jupyter Notebook](https://github.com/QIICR/dicom4miccai-handson/blob/master/notebooks/headneck.ipynb) we developed for DICOM4MICCAI 2017.


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