QIN multi-site collection of Lung CT data with Nodule Segmentations (QIN-LungCT-Seg)

This dataset (also known as the “moist run” among QIN sites) contains CT images (41 total scans) of non-small cell lung cancer from: the Reference Image Database to Evaluate Therapy Response (RIDER), the Lung Image Database Consortium (LIDC), patients from Stanford University Medical Center and the Moffitt Cancer Center, and the Columbia University/FDA Phantom. In addition, 3 academic institutions (Columbia, Stanford, Moffitt-USF) each ran their own segmentation algorithm on a total of 52 tumor volumes.  Segmentations were performed 3 different times with different initial conditions, resulting in 9 segmentations formatted as DICOM Segmentation Objects (DSOs) for each tumor volume, for a total of 468 segmentations. This collection may be useful for designing and comparing competing segmentation algorithms, for establishing acceptable ranges of variability in volume and segmentation borders, and for developing algorithms for creating cancer biomarkers from features computed from the segmented tumors and their environments.

本数据集（在QIN站点中亦被称为‘湿润试运行（moist run）’）涵盖来自以下来源的非小细胞肺癌CT影像，共计41次扫描：用于评估治疗反应的参考影像数据库（Reference Image Database to Evaluate Therapy Response, RIDER）、肺部影像数据库联盟（Lung Image Database Consortium, LIDC）、斯坦福大学医学中心患者、莫菲特癌症中心患者，以及哥伦比亚大学/美国食品药品监督管理局（Food and Drug Administration, FDA）体模。此外，3所学术机构（哥伦比亚大学、斯坦福大学、莫菲特-南佛罗里达大学（Moffitt-USF））分别针对总计52个肿瘤体积运行了各自的分割算法。分割操作在不同初始条件下重复开展3次，每个肿瘤体积对应9份以DICOM分割对象（DICOM Segmentation Objects, DSOs）格式存储的分割结果，总分割样本量达468份。该数据集可用于设计并对比各类竞争性分割算法、确定肿瘤体积与分割边界的可接受变异范围，以及开发基于分割肿瘤及其周边环境提取的特征构建癌症生物标志物的相关算法。

搜集汇总

背景与挑战

背景概述

该数据集是一个多站点收集的肺部CT数据集合，包含来自RIDER、LIDC等来源的41例非小细胞肺癌CT扫描图像，以及52个肿瘤体积的468个分割结果，这些分割以DICOM Segmentation Objects格式提供，并进行了多次不同初始条件下的分割。其主要特点在于整合了多源数据，并提供了丰富的分割结果，适用于设计比较分割算法、评估变异性范围以及开发癌症生物标志物算法。