  The Image Data Commons (IDC) contains publicly available cancer radiology
datasets that could be pertinent to the research and development of advanced
imaging tools and algorithms. However, the full extent of its research
capabilities is limited by the fact that these datasets have few if any,
annotations associated with them. Through this study with the Artificial
Intelligence (AI) AI in Medical Imaging (AIMI) initiative, we produced
high-quality, AI-generated imaging annotations of tissues, organs, and/or
cancers for 11 distinct medical imaging collections from the IDC. These
collections have a variety of image modalities, computed tomography (CT),
magnetic resonance imaging (MRI), and positron emission tomography (PET)
imaging modalities. Furthermore, the imaging collections cover various body
parities, such as the chest, breast, kidneys, prostate, and liver. Both
publicly available and novel AI algorithms were adopted and further developed
using open-sourced data coupled with expert annotations to create the
AI-generated annotations. A portion of the AI annotations were reviewed and
corrected by a radiologist to assess the AI model's performance. Both the AI's
and the radiologist's annotations conformed to DICOM standards for seamless
integration into the IDC collections as third-party analyses. All the models
and datasets (images and annotations) are publicly accessible.
