用AI为5类癌症影像生成高质量标注，助力医学影像研究
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
- 基于nnU-Net模型自动分割乳腺、脑、肝、肺、前列腺癌影像
- 11个IDC数据集完成标注，经放射科医生审核修正
- 所有数据符合DICOM标准，公开可获取，适合医疗AI研发
本项目旨在通过开发nnU-Net模型并提供人工智能辅助分割，提升美国国家癌症研究所（NCI）影像数据共享平台（IDC）的质量。我们为IDC的11个数据集创建了高质量的AI标注影像，涵盖CT和MRI等多种模态，覆盖肺、乳腺、脑、肾、前列腺和肝等器官。nnU-Net模型使用开源数据集训练，部分AI生成的标注由放射科医生进行人工审查与修正。所有AI及人工标注均按数字医学通信标准（DICOM）编码，确保与IDC数据集无缝集成。所有模型、图像与标注均已公开，供科研人员进一步研究与算法开发。该工作为癌症影像分析工具与算法的发展提供了全面且准确的标注数据支持。
原文摘要 · Abstract (English)
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.
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