TCIA_Notebooks - GitLite

KO | EN

gitlite — search

#python #ai #llm #go #machine-learning #linux #nodejs #macos #java #windows #awesome-list #security #mcp #openai

TCIA_Notebooks

(★ 136)

Notebooks for working with The Cancer Imaging Archive datasets. Have you written one? Submit a PR!

File Explorer

Download Latest Version (.zip)

- cptac-pan-cancer-drivers-table-S1A.xlsx
- CPTAC.ipynb
- Prostate-MRI-US-Biopsy-0001-ProstateSurface-seriesUID-1.3.6.1.4.1.14519.5.2.1.266717969984343981963002258381778490221.STL
- TCGA_Clinical.ipynb
.gitignore
Accessing_TCIA_Public_DICOM_data_in_IDC.ipynb
environment.yml
EnvironmentCheck.ipynb
LICENSE
README.md
TCIA_Aspera_CLI_Downloads.ipynb
TCIA_Data_Curation_Learning_Lab_SIIM_2024.ipynb
TCIA_DataCite_Queries.ipynb
TCIA_for_Georgetown_HIDS_2026.ipynb
TCIA_Image_Visualization_with_itkWidgets.ipynb
TCIA_Jupyter_Learning_Lab_2023.ipynb
TCIA_Linux_Data_Retriever_App.ipynb
TCIA_MedImageTools.ipynb
TCIA_MIRP.ipynb
TCIA_NCTN_Annotations.ipynb
TCIA_PathDB_Queries.ipynb
TCIA_Radiology_Inventory.ipynb
TCIA_REST_API_Downloads.ipynb
TCIA_REST_API_Queries.ipynb
TCIA_RTStruct_SEG_Visualization_with_itkWidgets.ipynb
TCIA_Segmentations.ipynb
TCIA_Series_UID_Report.ipynb
TCIA_STL_Visualization_with_itkWidgets.ipynb

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# TCIA_Notebooks
This is a home for notebooks which demonstrate how to access and work with TCIA datasets. Most of them heavily leverage functionality from [tcia_utils](https://pypi.org/project/tcia-utils/).

# General Notebooks
* [TCIA_for_Georgetown_HIDS_2026.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_for_Georgetown_HIDS_2026.ipynb) - A comprehensive tutorial presented to the Georgetown HIDS graduate student program which demonstrated how to access TCIA data from various system components. 
* Accessing TCIA's open-access DICOM data in IDC
  *  [Accessing_TCIA_Public_DICOM_data_in_IDC.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/Accessing_TCIA_Public_DICOM_data_in_IDC.ipynb) - A Python tutorial on how to use the NCI Imaging Data Commons idc-index package to query and download TCIA's open-access DICOM datasets.  This is ideal for those who prefer SQL queries.
  * [TCIA_REST_API_Downloads.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_REST_API_Downloads.ipynb) - A python tutorial for using tcia_utils idc.py for downloading TCIA's open-access DICOM datasets. This is helpful for users migrating code that used nbia.py or who prefer parameter-based Python functions for queries.
  * [TCIA_REST_API_Queries.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_REST_API_Queries.ipynb) - A python tutorial for using tcia_utils idc.py for additional search and reporting features to understand TCIA's open-access DICOM datasets. This is helpful for users migrating code that used nbia.py or who prefer parameter-based Python functions for queries.
* [TCIA_Aspera_CLI_Downloads.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_Aspera_CLI_Downloads.ipynb) - A short tutorial on how to download TCIA datasets that are made available through Aspera via the command line (rather than via the Aspera browser plugin).  TCIA typically uses Aspera for downloading histopathology collections or radiology collections that were provided in a format other than DICOM.
* [TCIA_DataCite_Queries.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_DataCite_Queries.ipynb) - TCIA issues a Digital Object Identifier (DOI) for each of its datasets through DataCite. This notebook demonstrates how the DataCite API can be used to programmatically access Collection metadata such as their DOI URL, title, publication date, licensing information and abstract.

# Collection-specific Notebooks
*  [CPTAC.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/CPTAC/CPTAC.ipynb) - A tutorial on accessing DICOM images and tumor annotations (3d segmentations & seed points) related to the [CPTAC-UCEC](https://doi.org/10.7937/89M3-KQ43) (Corpus Endometrial Carcinoma), [CPTAC-PDA](https://doi.org/10.7937/BW9V-BX61) (Pancreatic Ductal Adenocarcinoma), [CPTAC-CCRCC](https://doi.org/10.7937/SKQ4-QX48) (Clear Cell Renal Carcinoma), and [CPTAC-HNSCC](https://doi.org/10.7937/PFEC-T641) (Head and Neck Squamous Cell Carcinoma) datasets hosted on TCIA.
*  [TCIA_NCTN_Annotations.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_NCTN_Annotations.ipynb) - A tutorial on accessing DICOM images, clinical data, and tumor annotations (3d segmentations & seed points) related to [NCI Clinical Trial Network datasets](https://wiki.cancerimagingarchive.net/x/BQHDAg) hosted on TCIA.
*  [TCGA_Clinical.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCGA/TCGA_Clinical.ipynb) - A tutorial on accessing DICOM images and clinical data for patients from [The Cancer Genome Atlas Program](https://wiki.cancerimagingarchive.net/x/sgEe).

# AI and Visualization Notebooks
*  [TCIA_Segmentations](https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_Segmentations.ipynb) - A Python tutorial focused on using the TCIA APIs to identify segmentation data, find the corresponding reference series and visualize them together. 
* [TCIA_Image_Visualization_with_itkWidgets.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_Image_Visualization_with_itkWidgets.ipynb) - Example of downloading TCIA DICOM images and visualizing them as interactive cinematic volume renderings or as 2D slices, using [itkWidgets](https://github.com/InsightSoftwareConsortium/itkwidgets).
* [TCIA_PROSTATEx_Prostate_MRI_Anatomy_Model.ipynb](https://github.com/Project-MONAI/tutorials/blob/main/model_zoo/TCIA_PROSTATEx_Prostate_MRI_Anatomy_Model.ipynb) - Demonstrates downloading data from TCIA, downloading a pre-trained model from [MONAI's Model Zoo](https://monai.io/model-zoo.html), applying the model to the data to segment anatomic structures in that data, and then visually comparing model results with expert segmentations.
* [TCIA_RTStruct_SEG_Visualization_with_itkWidgets.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_RTStruct_SEG_Visualization_with_itkWidgets.ipynb) - Tutorial of downloading expert annotations as DICOM SEG and RTSTRUCT objects, converting them to labelmaps for use in training and evaluating AI models, and visualizing them with their source images in 3D or as overlays on 2D slices.
* [TCIA_STL_Visualization_with_itkWidgets.ipynb](https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_STL_Visualization_with_itkWidgets.ipynb) - Shows how to download, convert, and visualize expert annotations and 3D printer models stored in STL format on TCIA for use in training and evaluating AI models.