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🚀 Getting started

We want Imaging Data Commons to be your companion in your cancer imaging research activities - from discovering relevant data to sharing your analysis results and showcasing the tools you developed!

Explore the data available

Check out quick instructions on how to access and use IDC Portal web application that will help you search, subset and visualize data available in IDC.

IDC Portal is integrated with powerful visualization tools: just with your web browser you will be able to see IDC images and annotations using OHIF Viewer, Slim viewer and VolView!

Subset the content you need

We have many tools to help you search data in IDC, so that you download only what you need!

  • you can do basic filtering/subsetting of the data using IDC Portal, but if you are developer, you will want to learn how to use idc-index python package for programmatic access. This python notebook will introduce you to the basics of idc-index for interaction with IDC content.
  • search clinical data: many of the IDC collections are accompanied by clinical data, which we parsed for you into searchable tabular representation - no need to download or parse CSV/Excel/PDF files! Dive into searching clinical data using this notebook.
  • if advanced content does not scare you, check out this notebook to learn how to search all of the metadata accompanying IDC using SQL and Google BigQuery.

Download the data you liked

We provide various tools for downloading data from IDC, as discussed in the Download documentation page. Access to all data in IDC is free! No registration. No access request forms. No logins.

  • once you have idc-index python package installed, download from the command line is as easy as running idc download <manifest_file>, or idc download <collection_id>.
  • looking for an interactive "point-and-click" application? 3D Slicer IDC Browser extension is for you (note that you will only be able to visualize radiology - not microscopy - images in 3D Slicer)

Experiment with analysis tools

We want to make it easier to understand performance of the latest advances in AI on real-world cancer imaging data!

  • if you have a Google account, you have free access to Google Colab, which allows you to run python notebooks on cloud VMs equipped with GPU - for free! Combined with idc-index for data access, this makes it rather easy to experiment with the latest AI tools! As an example, take a look at this notebook that allows you to apply MedSAM model to IDC data. You will find a growing number of notebooks to help you use IDC in this repository.
  • use IDC to develop HuggingFace spaces that demonstrate the power of your models on real data: see this space we developed for SegVol
  • growing number of AI medical imaging models is being curated on the MHub.ai platform; see this notebook to learn how to apply those models on data from IDC

How about accompanying your next publication by a working demonstration notebook on relevant samples from IDC? You can see an example how we did this in this recent publication.

Scale the analysis to thousands of cloud VMs

With the cloud, you can do things that are simply impossible to do with your local resources.

  • read this preprint to learn how we applied TotalSegmentator+pyradiomics to >126,000 of CT scans of the NLST collection using Terra platform, completing the analysis in ~8 hours with the total cost ~$1000
  • this repository contains the code we used in the above (this is really advanced content!)

Share analysis results or annotations

If you have an algorithm, that you evaluated/published, that can enrich data in IDC with analysis results and you want to contribute those, or if you are a domain expert and would like to publish results of manual annotations you prepared - we want to hear from you!

  • IDC maintains a Zenodo community where we curate contributions of analysis results and other datasets produced by IDC (see the expert annotations of the RMS-Mutations-Prediction microscopy images collection as one example of such contribution)
  • through a dedicated Zenodo record you will have a citation and DOI to get credit for your work; your data is ingested from Zenodo into IDC, and citation will be generated for the users of your data in IDC
  • once your data is in IDC, it should be easier to discover it, combine with other datasets, visualize and use from analysis workflows (as an example, see this notebook accompanying the RMS annotations)
  • email us at [email protected] to inquire about contributing your annotations/analysis results to IDC!

Questions?

Join IDC forum with any inquiries about IDC - we want to hear from you! As you will see from the historical posts, we typically respond to user questions very quickly.