在国家肺部筛查试验计算机断层扫描图像中标注解剖结构和病理
Annotating anatomy and pathology in the National Lung Screening Trial computed tomography images
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中文总结 AI 辅助
针对NLST CT影像，提出三个DICOM格式标注数据集，包含解剖与病理标注、测量及分割和影像组学特征，以增强数据可用性并支持图像分析工具开发。
中文摘要 AI 辅助
大规模公共医学影像数据集对转化研究具有关键贡献。当这些数据集伴随丰富的临床和多组学数据时，可以激发探索性研究并支持二次分析。对此类影像集合的专家标注能够支持新图像分析工具的开发。用图像衍生数据持续丰富图像，使其对缺乏图像分析专业知识或无法访问大规模计算资源的研究人员更具可用性。国家肺部筛查试验（NLST）发布了一个丰富的纵向数据集，其中包括超过26,000名患者的计算机断层扫描（CT）图像。我们引入了三个数字成像和通信医学（DICOM）格式的数据集，作为对NLST CT图像的补充，并以分析结果的形式共享于国家癌症研究所影像数据共享平台（IDC）。其中两个数据集（IDC NLSTSeg和IDC NLSTSybil）包含DICOM协调的标注和提取的测量结果（分别针对581名和601名NLST患者），这些结果此前以研究格式（Sybil和NLSTseg）共享。第三个数据集（TotalSegmentator-CT-Segmentations）包含使用TotalSegmentator生成的体积分割结果，以及针对26,194名NLST患者每个分割区域的影像组学特征。
英文摘要
Large-scale public medical imaging datasets contribute critically to translational research. When accompanied by rich clinical and multi-omics data, they can stimulate exploratory research and enable secondary analyses. Expert annotations of such imaging collections can support the development of new image analysis tools. Continuous enrichment of images with image-derived data makes them more usable for researchers without expertise in image analysis or access to large-scale computational resources. The National Lung Screening Trial (NLST) released a rich longitudinal dataset that includes Computed Tomography (CT) images for over 26,000 patients. We introduce three Digital Imaging and Communications in Medicine (DICOM) formatted datasets, complementing NLST CT images, shared as analysis results in the National Cancer Institute Imaging Data Commons (IDC). Two of those (IDC NLSTSeg and IDC NLSTSybil) contain DICOM-harmonized annotations and extracted measurements (for 581 and 601 NLST patients, respectively) shared earlier using research formats (Sybil and NLSTseg). The third one (TotalSegmentator-CT-Segmentations) contains volumetric segmentations generated using TotalSegmentator and radiomics features for each segment for 26,194 NLST patients.
