Murugesan Gowtham Krishnan, McCrumb Diana, Soni Rahul, Kumar Jithendra, Nuernberg Leonard, Pei Linmin, Wagner Ulrike, Granger Sutton, Fedorov Andrey Y, Moore Stephen, Van Oss Jeff
BAMF Health, Grand Rapids, MI, USA.
Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Sci Data. 2025 Jul 29;12(1):1317. doi: 10.1038/s41597-025-05666-6.
The Artificial Intelligence in Medical Imaging (AIMI) initiative aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by releasing fully reproducible nnU-Net models, along with AI-assisted segmentation for cancer radiology images. In this extension of our earlier work, we created high-quality, AI-annotated imaging datasets for 11 IDC collections, spanning computed tomography (CT) and magnetic resonance imaging (MRI) of the lungs, breast, brain, kidneys, prostate, and liver. Each nnU-Net model was trained on open-source datasets, and a portion of the AI-generated annotations was reviewed and corrected by board-certified radiologists. Both the AI and radiologist annotations were encoded in compliance with the Digital Imaging and Communications in Medicine (DICOM) standard, ensuring seamless integration into the IDC collections. By making these models, images, and annotations publicly accessible, we aim to facilitate further research and development in cancer imaging.
医学影像人工智能（AIMI）计划旨在通过发布完全可重现的nnU-Net模型以及癌症放射影像的人工智能辅助分割，来增强美国国立癌症研究所（NCI）的图像数据共享库（IDC）。在我们早期工作的这个扩展中，我们为11个IDC数据集创建了高质量的、由人工智能标注的成像数据集，涵盖肺部、乳腺、大脑、肾脏、前列腺和肝脏的计算机断层扫描（CT）和磁共振成像（MRI）。每个nnU-Net模型都在开源数据集上进行训练，并且一部分由人工智能生成的标注由经过委员会认证的放射科医生进行了审查和校正。人工智能和放射科医生的标注均按照医学数字成像和通信（DICOM）标准进行编码，以确保无缝集成到IDC数据集中。通过使这些模型、图像和标注公开可用，我们旨在促进癌症成像领域的进一步研究和开发。
