Objective: Research in Artificial Intelligence (AI) for medical imaging requires large volumes of high-quality, labelled data. The Cancer Imaging Archive (TCIA) is a public repository of DICOM images related to oncology. The aim of this work is to provide researchers and developers with a simple way to import images from the TCIA servers onto a local PACS environment.
Methods: TCIA provides a network interface (REST API) that enables third-party applications to programmatically access its collections. Orthanc is an open-source DICOM server that can be deployed by research teams as their PACS server. The deliverable of this work is an original, open-source plugin for Orthanc that imports images from TCIA using its REST API.
Results: The developed plugin takes the form of an easy-to-use Web application to browse TCIA collections and import their images. The imported images are served according to the DICOM standard, and can be displayed using zero-footprint viewers.
Conclusion: This work proposes a solution to import real-world, open-data medical images into an environment that mimics clinical setups, which is essential to AI research. Those images come from TCIA, that contains multiple acquisitions of various body parts acquired by different modalities, making it useful in many fields of radiology.

**Objective:** Research in Artificial Intelligence (AI) for medical imaging requires large volumes of high-quality, labelled data. The Cancer Imaging Archive (TCIA) is a public repository of DICOM images related to oncology. The aim of this work is to provide researchers and developers with a simple way to import images from the TCIA servers onto a local PACS environment.
**Methods:** TCIA provides a network interface (REST API) that enables third-party applications to programmatically access its collections. Orthanc is an open-source DICOM server that can be deployed by research teams as their PACS server. The deliverable of this work is an original, open-source plugin for Orthanc that imports images from TCIA using its REST API.
**Results:** The developed plugin takes the form of an easy-to-use Web application to browse TCIA collections and import their images. The imported images are served according to the DICOM standard, and can be displayed using zero-footprint viewers.
**Conclusion:** This work proposes a solution to import real-world, open-data medical images into an environment that mimics clinical setups, which is essential to AI research. Those images come from TCIA, that contains multiple acquisitions of various body parts acquired by different modalities, making it useful in many fields of radiology.

Importing and serving open-data medical images to support Artificial Intelligence research

[Networking hour](https://app.wonder.me/?spaceId=fe093845-759f-448f-9f55-01d3adccd820)
[COIs](https://drive.google.com/drive/folders/1nbfwAa9lf9Kz-wAZ7iLKhfpBFjpN_gkn?usp=sharing)