DICOM4MICCAI Hands-on
  • Introduction
  • Prerequisites
    • If you are attending the tutorial in-person
    • If you are following on your own
  • Using DICOM to store your analysis results
    • Using 3D Slicer to convert non-DICOM segmentation results
    • DICOM Structured Reporting for radiomics
    • Using 3D Slicer for storing analysis results in DICOM
      • Step 0: 3D Slicer interface basics
      • Step 1: Import DICOM data
      • Step 2: Load DICOM image
      • Step 3: Segment lesions
      • Step 3.1:QuantitativeReporting interface overview
      • Step 3.2: Create and initialize a new segment
      • Step 3.3: Segment the lesions
      • Step 4: Explore and store the analysis results in DICOM
      • Step 5: Reload the analysis results from DICOM
      • Exporting DICOM data from 3D Slicer
    • Using MITK Workbench for storing segmentation results in DICOM
      • Step 0: MITK interface basics
      • Step 1: Import DICOM data
      • Step 2: Load DICOM image
      • Step 3: Segment lesions
      • Step 4: Explore and store the segmentations in DICOM
      • Step 5: Reload the segmentations from DICOM
  • DICOM data wrangling
  • Further reading
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  • Hands-on demo notes
  • QIN-HEADNECK collection exploration

DICOM data wrangling

PreviousStep 5: Reload the segmentations from DICOMNextFurther reading

Last updated 6 years ago

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

    • (annotations in XML)

    • ()

    • ()

    • QIN-PROSTATE-Repeatability (not yet released)

  • discuss the example dataset used in the demo

  • steps for handling DICOM data:

    • for converting image series into volumes

    • for working with SEG and SR

    • : conversion into tabular representation for working with metadata

      • "database"

    • Jupyter Notebook demonstration

This part will be covered in .

QIN-HEADNECK collection exploration

See we developed for DICOM4MICCAI 2017.

LIDC-IDRI
TCGA-GBM
annotations in NIfTI
TCGA-LGG
annotations in NIfTI
QIN-HEADNECK
dicomsort
dcm2niix
dcmqi
dcm2tables
visual schema
this Jupyter Notebook
this Jupyter Notebook