The Lung Image Database Consortium image collection (LIDC-IDRI)

The Lung Image Database Consortium image collection (LIDC-IDRI) consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.Seven academic centers and eight medical imaging companies collaborated to create this data set which contains 1018 cases. Each subject includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (nodule or =3 mm, nodule 3 mm, and non-nodule or =3 mm). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.

肺影像数据库联盟图像集（LIDC-IDRI）包含标注有病变的诊断性及肺癌筛查用胸部计算机断层扫描（CT）影像。该数据集为可通过网络访问的国际化资源，用于开发、训练及评估用于肺癌检测与诊断的计算机辅助诊断（CAD）方法。本项目由美国国家癌症研究所（NCI）发起，美国国立卫生研究院基金会（FNIH）推进完善，并由美国食品药品监督管理局（FDA）积极参与协作，这一公私合作模式彰显了基于共识流程组建的联盟所取得的成功。7家学术机构与8家医学影像企业合作构建了该数据集，共包含1018个病例。每一例受试者均包含临床胸部CT扫描影像，以及关联的可扩展标记语言（XML）文件，该文件记录了由4名经验丰富的胸部放射科医师完成的两阶段图像标注流程结果。在初始盲读阶段，每位放射科医师独立审阅每一份CT扫描影像，并将病变划分为三类：≥3mm结节、＜3mm结节以及≥3mm非结节病变。在后续的非盲读阶段，每位放射科医师在审阅自身标注结果的同时，参考另外三位放射科医师的匿名标注结果，最终给出个人诊断意见。该流程的目标是在无需强制达成共识的前提下，尽可能全面地识别每一份CT扫描影像中的所有肺结节。