标题： 用于腹部CT可变形图像配准（DIR）验证的血管分叉地标对数据集 显示英文标题

标题： A Vessel Bifurcation Landmark Pair Dataset for Abdominal CT Deformable Image Registration (DIR) Validation

摘要： 可变形图像配准（DIR）是许多诊断和治疗任务中的关键技术。尽管如此，DIR算法的临床应用有限，主要是因为在开发过程中缺乏用于质量保证的基准数据集。为了支持未来的算法开发，我们介绍了首个针对腹部CT DIR的基准数据集，该数据集包含大量在匹配血管分叉处的高精度地标对。30名患者的腹部CT图像对是从多个公共存储库以及作者机构在IRB批准下获取的。每对CT图像最初是为同一名患者在不同日期获取的。为每对图像开发并应用了图像处理流程：1）使用深度学习模型对腹部器官进行分割，并覆盖器官掩码内的图像强度。2）在每对图像的两个CT之间手动识别匹配的图像块。3）在每对图像块的一个图像上标记血管分叉地标的。4）对图像块进行可变形配准，并将地标的投影到第二个图像上。5）手动或通过自动化过程精炼地标的对位置。该流程共产生了1895个地标的对，平均每个案例63个。使用数字幻像估计的地标的对精度为0.7+/-1.2mm。数据已在Zenodo发布，网址为https://doi.org/10.5281/zenodo.14362785。使用说明可在https://github.com/deshanyang/Abdominal-DIR-QA找到。该数据集是首个用于腹部DIR验证的数据集。地标的数量、精度和分布将允许对DIR算法进行稳健的验证，其精度超过目前可用的水平。 显示英文摘要

摘要： Deformable image registration (DIR) is an enabling technology in many diagnostic and therapeutic tasks. Despite this, DIR algorithms have limited clinical use, largely due to a lack of benchmark datasets for quality assurance during development. To support future algorithm development, here we introduce our first-of-its-kind abdominal CT DIR benchmark dataset, comprising large numbers of highly accurate landmark pairs on matching blood vessel bifurcations. Abdominal CT image pairs of 30 patients were acquired from several public repositories as well as the authors' institution with IRB approval. The two CTs of each pair were originally acquired for the same patient on different days. An image processing workflow was developed and applied to each image pair: 1) Abdominal organs were segmented with a deep learning model, and image intensity within organ masks was overwritten. 2) Matching image patches were manually identified between two CTs of each image pair 3) Vessel bifurcation landmarks were labeled on one image of each image patch pair. 4) Image patches were deformably registered, and landmarks were projected onto the second image. 5) Landmark pair locations were refined manually or with an automated process. This workflow resulted in 1895 total landmark pairs, or 63 per case on average. Estimates of the landmark pair accuracy using digital phantoms were 0.7+/-1.2mm. The data is published in Zenodo at https://doi.org/10.5281/zenodo.14362785. Instructions for use can be found at https://github.com/deshanyang/Abdominal-DIR-QA. This dataset is a first-of-its-kind for abdominal DIR validation. The number, accuracy, and distribution of landmark pairs will allow for robust validation of DIR algorithms with precision beyond what is currently available.