A Data Analysis Center (DAC) is a tool or website which provides additional capabilities for downloading, visualizing or analyzing TCIA data by connecting to our TCIA Programmatic Interface (REST API) or by mirroring our Collections. Learn more about how DACs make it easier to work with our datasets in this presentation.
If you have developed something which meets the DAC criteria please contact the helpdesk so we can add it to this page. We will also work with you to ensure your site/tool provides adequate attributions and links back to TCIA to comply with our Data Usage Policies and Restrictions.
| Resource | Description | Functionality | TCIA Data Access | Platform |
|---|---|---|---|---|
| 3D Slicer TCIA Browser extension | 3D Slicer (http://slicer.org) is a free and open source platform for medical image visualization and quantitative analysis. The TCIA Browser extension of 3D Slicer enables integration of the versatile visualization and computing tools of 3D Slicer with unique data resources of TCIA. Among other capabilities, 3D Slicer enables 2-, 3-, and 4-d visualization tools, DICOM interoperability for both images and image annotations, radiomics feature calculation, multi-modality fusion and deformable registration, a collection of segmentation tools, Matlab and python interface, and integration of such libraries as ITK, VTK, DCMTK and numpy. | Visualization and Analysis | API | Windows, Mac OS, Linux |
| CancerImagingArchive.jl | Julia interface for exploring and downloading data on The Cancer Imaging Archive (TCIA) | Data access | API | Windows, Mac OS, Linux |
| Childhood Cancer Data Catalog | A searchable database of pediatric data resources, sharing clinical care and research data generated by the pediatric cancer research community. The site includes entries for all of TCIA's pediatric datasets. | Data access | Catalog | Web application |
| ChimeraX | UCSF ChimeraX is the next-generation molecular and medical image visualization program from the Resource for Biocomputing, Visualization, and Informatics (RBVI), following UCSF Chimera. ChimeraX can be downloaded free of charge for academic, government, nonprofit, and personal use. Data can be viewed on a graphical desktop or, on Windows, in VR. Documentation on how to access TCIA data directly from within ChimeraX can be found at https://www.rbvi.ucsf.edu/chimerax/docs/user/tools/downloaddicom.html. | Data access, Visualization and Analysis | API | Windows, Mac OS, Linux |
| Community Code Share on Github | If you've developed open source code you'd like to share with the community you can use Github's topic feature to make it discoverable by tagging it with "tcia-dac". Please note these tools are not directly supported by TCIA or its helpdesk. | Data access, Visualization, and Analysis | API / Mirrored | Miscellaneous |
| DataCite Commons | Each Collection TCIA publishes is issued a Digital Object Identifier (DOI) through DataCite. The DataCite Commons is a web search interface for the PID Graph, the graph formed by the collection of scholarly resources such as publications, datasets, people and research organizations, and their connections. The PID Graph uses persistent identifiers and GraphQL, with PIDs and metadata provided by DataCite, Crossref, ORCID, and others. | Data access | Mirrored | Web application |
| DICOMScanClassification | With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial intelligence-based tasks. Incomplete or missing metadata prevents effective automation, leading to time-consuming and error-prone processes by clinicians. We propose a deep-learning method for classification of prostate cancer scanning sequences based on a combination of image data and DICOM metadata. We demonstrate superior results compared to metadata or image data alone, and provide series-level "ground truth" assignments for several TCIA prostate collections. | Analysis | N/A | Python, Cloud-based Platform |
| EuCanImage Catalog | EuCanImage is a research project funded by the European Commission, which is developing a European cancer imaging platform and new Artificial Intelligence solutions for oncology. The EuCanImage catalog ( https://molgenis.eibir-edc.org/#/) integrated all TCIA public collections as well as a rapidly growing number of EU data collections. Hence, it is now possible for researchers and developers to search through the EuCanImage catalog based in Europe and identify TCIA imaging collections based in the US. The EuCanImage catalog provides metadata summaries for each collection and links directly to TCIA download pages. | Data Access | Catalog | Web application |
| FAST Data Hub | The FAST Data Hub contains open data which can be downloaded and used to test functionality of the high performance medical imaging framework FAST. The FAST Data Hub contains a few selected images from The Cancer Imaging Archive (TCIA). | Data access, Visualization and Analysis | Mirrored | Windows, Linux, Mac |
| G-DOC Plus | The Georgetown Database of Cancer Plus other diseases (G-DOC Plus) is a precision medicine platform containing molecular and clinical data from thousands of patients and cell lines, along with tools for analysis and data visualization. It contains mirrored data from the BREAST-DIAGNOSIS collection. | Visualization and Analysis | Mirrored | Web application |
| MONAI | MONAI (https://monai.io) is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides deep learning capabilities tailored for healthcare imaging research, development, and deployment. MONAI is part of the larger Project MONAI effort, that includes MONAI Label for AI assisted annotation; MONAI Deploy for packaging, distributing, and deploying MONAI-based applications; and MONAI Tutorials which contains extensive educational and community building resources. MONAI's TCIA Dataset Tutorial describes how patient data (images, lab results, etc.) from NCI repositories such as the IDC and TCIA can be leveraged for MONAI model development and deployment. MONAI is available on cloud services such as Google CoLab and Amazon Sagemaker and across every major operating system and Python version via pip. MONAI Label is also available as a 3D Slicer Plugin. | Data access, Visualization, and Analysis | API | Windows, Mac OS, Linux |
| NCI Imaging Data Commons | NCI Imaging Data Commons (IDC) is a cloud-based resource within NCI Cancer Research Data Commons (CRDC) that connects researchers with cancer imaging datasets, resources for exploring those datasets and identifying relevant cohorts, and other components of CRDC that will host additional data types and support computation on the defined cohorts. | Visualization and Analysis | Mirrored | Cloud-based platform |
| Oncora Medical TCIA boostrapper | Repository with minimal docker compose configuration and script to create a DICOM server with a TCIA collection locally. Can be extended modularly with additional docker images for deep learning experiments. | Data Access | API | Windows, Mac OS, Linux |
| Orthanc TCIA Plugin | This plugin extends Orthanc with a Web interface that can be used to import open-data medical images from The Cancer Imaging Archive (TCIA), and serve them immediately using Orthanc. The plugin can be used to import so-called “cart spreadsheet” generated by the NBIA Search Client, or to browse the image collections of TCIA thanks to its REST API. | Data Access | API / Mirrored | Windows, Mac OS, Linux |
| pylidc | pylidc is a python library intended to improve workflow associated with the LIDC dataset. | Visualization and Analysis | N/A | Windows, Mac OS, Linux |
| Semantic Search Cohort Builder | An interactive Cancer Imaging Archive Semantic Search Cohort Builder web application. | Data Access, Visualization, Analysis | N/A | Web application |
| Shared Cart Creator | A convenient web interface to generate a "Shared Cart" using Series Instance UIDs. This is helpful if you've downloaded data and performed local curation to weed out unnecessary scans, but now want to create a URL to make it easy to share that specific set of scans with others (e.g. as part of a manuscript). The URL it generates will take people to a TCIA page where users can preview your selected scans in their browser or download them. | Data Access, Visualization | API | Web application |
| Seven Bridges Cancer Genomics Cloud (CGC) | An NCI-funded platform that is available to any non-commercial researcher for cloud-based data access and analysis. Through the CGC, users can access petabytes of public data, including select collections from TCIA, as well as hundreds of bioinformatic tools and workflows for scalable, cost-effective analysis in the cloud alongside their own data. | Data Access, Visualization, Analysis | Mirrored | Web application |
| SIIM Hackathon Dataset | The SIIM Hackathon Dataset is a small but comprehensive dataset featuring fictional, but realistic, patient personas with their clinical data in FHIR and images in DICOM that corroborate each patient's story. Additionally, the dataset features diverse, but clinically relevant, samples of DICOM files from medical specialities like Radiology, Pathology, Dermatology, Ophthalmology, Radiation Therapy and Dentistry. | Data Access | Mirrored | Windows, Mac OS, Linux |
| tcia_utils | This PyPI package contains functions to simplify common tasks one might perform when interacting with The Cancer Imaging Archive (TCIA) APIs via Jupyter/Python. | Data Access | API | Windows, Mac OS, Linux |
| TCIApathfinder | A user-friendly R client for the TCIA REST API | Data access | API | Windows, Mac OS, Linux |