AI in Medical Imaging project aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by developing nnU-Net models and providing AI-assisted segmentations for cancer radiology images. We created high-quality, AI-annotated imaging datasets for 11 IDC collections. These datasets include images from various modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), covering the lungs, breast, brain, kidneys, prostate, and liver. The nnU-Net models were trained using open-source datasets. A portion of the AI-generated annotations was reviewed and corrected by radiologists. Both the AI and radiologist annotations were encoded in compliance with the the Digital Imaging and Communications in Medicine (DICOM) standard, ensuring seamless integration into the IDC collections. All models, images, and annotations are publicly accessible, facilitating further research and development in cancer imaging. This work supports the advancement of imaging tools and algorithms by providing comprehensive and accurate annotated datasets.

本研究解决了国家癌症研究所成像数据共享平台（IDC）中癌症影像数据集注释不足的问题。通过开发nnU-Net模型，本研究提供了高质量的AI辅助标注数据集，涵盖多种影像学模式，并经过放射科医生的审核与纠正。该研究的显著发现是，提供具有全面性和准确性的影像数据集，有助于推动癌症成像工具和算法的进一步研究与发展。

国家癌症研究所成像数据共享平台中AI生成的乳腺、脑、肝、肺和前列腺癌注释