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.
- TCIA as a use case
- LIDC-IDRI (annotations in XML)
- TCGA-GBM (annotations in NIfTI)
- TCGA-LGG (annotations in NIfTI)
- QIN-HEADNECK
- QIN-PROSTATE-Repeatability (not yet released)
- discuss the example dataset used in the demo
- steps for handling DICOM data:
- dicomsort
- dcm2niix for converting image series into volumes
- dcmqi for working with SEG and SR
- dcm2tables: conversion into tabular representation for working with metadata
- "database" visual schema
- Jupyter Notebook demonstration
This part will be covered in this Jupyter Notebook.
See this Jupyter Notebook we developed for DICOM4MICCAI 2017.