idc-index 0.5.6

Package to query and download data from an index of ImagingDataCommons

idc-index

[!WARNING]

This package is in its early development stages. Its functionality and API will change.

Stay tuned for the updates and documentation, and please share your feedback about it by opening issues in this repository, or by starting a discussion in IDC User forum.

About

idc-index is a Python package that enables query of the basic metadata and download of DICOM files hosted by the NCI Imaging Data Commons (IDC).

Usage

There are no prerequisites - just install the package ...

$ pip install idc-index

... and download files corresponding to any collection, DICOM PatientID/Study/Series as follows:

from idc_index import index

client = index.IDCClient()

all_collection_ids = client.get_collections()

client.download_from_selection(collection_id="rider_pilot", downloadDir="/some/dir")

... or run queries against the "mini" index of Imaging Data Commons data!

from idc_index import index

client = index.IDCClient()

query = """
SELECT
  collection_id,
  STRING_AGG(DISTINCT(Modality)) as modalities,
  STRING_AGG(DISTINCT(BodyPartExamined)) as body_parts
FROM
  index
GROUP BY
  collection_id
ORDER BY
  collection_id ASC
"""

client.sql_query(query)

Details of the attributes included in the index are in the release notes.

Tutorial

This package was first presented at the 2023 Annual meeting of Radiological Society of North America (RSNA) Deep Learning Lab IDC session.

Please check out this tutorial notebook for the introduction into using idc-index for navigating IDC data.

Resources

• Imaging Data Commons Portal can be used to explore the content of IDC from the web browser
• s5cmd is a highly efficient, open source, multi-platform S3 client that we use for downloading IDC data, which is hosted in public AWS and GCS buckets. Distributed on PyPI as s5cmd.
• SlicerIDCBrowser 3D Slicer extension that relies on idc-index for search and download of IDC data

Acknowledgment

This software is maintained by the IDC team, which has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071 under contract no. HHSN261201500003l.

If this package helped your research, we would appreciate if you could cite IDC paper below.

Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. RadioGraphics (2023). https://doi.org/10.1148/rg.230180