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.

翻译：医学影像AI项目旨在通过开发nnU-Net模型并为癌症放射影像提供AI辅助分割，以增强国家癌症研究所（NCI）影像数据共享库（IDC）。我们为IDC的11个数据集创建了高质量的AI标注影像数据集。这些数据集包含来自多种成像模态的图像，例如计算机断层扫描（CT）和磁共振成像（MRI），涵盖肺部、乳腺、脑部、肾脏、前列腺和肝脏。nnU-Net模型使用开源数据集进行训练。部分AI生成的标注经过放射科医师审核和修正。AI标注与放射科医师标注均按照医学数字成像与通信（DICOM）标准进行编码，确保可无缝集成到IDC数据集中。所有模型、图像及标注均公开可访问，以促进癌症影像领域的进一步研究与开发。本研究通过提供全面且准确的标注数据集，支持影像工具与算法的进步。