The AIMI Initiative: AI-Generated Annotations for Imaging Data Commons Collections

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 AI in Medical Imaging (AIMI) initiative a significant contribution, in the form of AI-generated annotations, was made to provide 11 distinct medical imaging collections from the IDC with annotations. These collections included computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) imaging modalities. The main focus of these annotations were in 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 models' performances. Both the AI's and the radiologist's annotations conformed to DICOM standards for seamless integration into the IDC collections as third-party analyses. This study further cements the well-documented notion that expansive publicly accessible datasets, in the field of cancer imaging, coupled with AI will aid in increased accessibility as well as reliability for further research and development.