PurposeDeformable image registration (DIR) is a key enabling technology in many diagnostic and therapeutic tasks, but often does not meet the required robustness and accuracy for supporting clinical tasks. This is in large part due to a lack of high‐quality benchmark datasets by which new DIR algorithms can be evaluated. Our team was supported by the National Institute of Biomedical Imaging and Bioengineering to develop DIR benchmark dataset libraries for multiple anatomical sites, comprising of large numbers of highly accurate landmark pairs on matching blood vessel bifurcations. Here we introduce our lung CT DIR benchmark dataset library, which was developed to improve upon the number and distribution of landmark pairs in current public lung CT benchmark datasets.Acquisition and Validation MethodsThirty CT image pairs were acquired from several publicly available repositories as well as authors’ institution with IRB approval. The data processing workflow included multiple steps: (1) The images were denoised. (2) Lungs, airways, and blood vessels were automatically segmented. (3) Bifurcations were directly detected on the skeleton of the segmented vessel tree. (4) Falsely identified bifurcations were filtered out using manually defined rules. (5) A DIR was used to project landmarks detected on the first image onto the second image of the image pair to form landmark pairs. (6) Landmark pairs were manually verified. This workflow resulted in an average of 1262 landmark pairs per image pair. Estimates of the landmark pair target registration error (TRE) using digital phantoms were 0.4 mm ± 0.3 mm.Data Format and Usage NotesThe data is published in Zenodo at https://doi.org/10.5281/zenodo.8200423. Instructions for use can be found at https://github.com/deshanyang/Lung‐DIR‐QA.Potential ApplicationsThe dataset library generated in this work is the largest of its kind to date and will provide researchers with a new and improved set of ground truth benchmarks for quantitatively validating DIR algorithms within the lung.

中文翻译：

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用于评估变形图像配准算法的综合肺部 CT 标志对数据集

目的可变形图像配准 (DIR) 是许多诊断和治疗任务中的关键支持技术，但通常无法满足支持临床任务所需的稳健性和准确性。这在很大程度上是由于缺乏可以评估新 DIR 算法的高质量基准数据集。我们的团队得到了国家生物医学成像和生物工程研究所的支持，为多个解剖部位开发了 DIR 基准数据集库，其中包含大量匹配血管分叉的高精度地标对。在这里，我们介绍了我们的肺部 CT DIR 基准数据集库，该库的开发是为了改进当前公共肺部 CT 基准数据集中标志对的数量和分布。 获取和验证方法从几个公开可用的存储库以及作者获取了 30 个 CT 图像对' 经 IRB 批准的机构。数据处理工作流程包括多个步骤：（1）图像去噪。(2)自动分割肺、气道、血管。(3)在分段血管树的骨架上直接检测分叉。(4)使用手动定义的规则过滤掉错误识别的分叉。(5)使用DIR将第一幅图像上检测到的地标投影到图像对的第二幅图像上以形成地标对。(6)人工验证地标对。此工作流程平均为每个图像对生成 1262 个地标对。使用数字模型对地标对目标配准误差 (TRE) 的估计值为 0.4 毫米 ± 0.3 毫米。数据格式和使用说明数据发表在 Zenodo 上https://doi.org/10.5281/zenodo.8200423。使用说明可以在以下位置找到https://github.com/deshanyang/Lung‐DIR‐QA潜在应用这项工作中生成的数据集库是迄今为止同类中最大的，将为研究人员提供一套新的、改进的地面实况基准，用于定量验证肺内的 DIR 算法。