Criscuolo Edward R, Zhang Zhendong, Hao Yao, Yang Deshan
Department of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.
Med Phys. 2025 Jul;52(7):e17907. doi: 10.1002/mp.17907. Epub 2025 May 28.
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. DIRs of intra-patient abdominal CTs are among the most challenging registration scenarios due to significant organ deformations and inconsistent image content. 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 publicly available repositories as well as the authors' institution with IRB approval. The two CTs of each pair were originally acquired for the same patient but on different days. An image processing workflow was developed and applied to each CT 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 mm ± 1.2 mm.
The data are 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.
可变形图像配准（DIR）是许多诊断和治疗任务中的一项赋能技术。尽管如此，DIR算法在临床上的应用仍然有限，这主要是由于在开发过程中缺乏用于质量保证的基准数据集。由于器官显著变形和图像内容不一致，患者腹部CT的DIR是最具挑战性的配准场景之一。为了支持未来的算法开发，我们在此引入首个腹部CT DIR基准数据集，该数据集包含大量匹配血管分叉处的高精度地标对。
从多个公开可用的数据库以及作者所在机构获取了30名患者的腹部CT图像对，并获得了机构审查委员会（IRB）的批准。每对中的两张CT最初是为同一患者在不同日期采集的。开发了一种图像处理工作流程并应用于每对CT图像：（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）在每对图像的两张CT之间手动识别匹配的图像块。（3）在每个图像块对的一张图像上标记血管分叉地标。（4）对图像块进行可变形配准，并将地标投影到第二张图像上。（5）手动或通过自动化过程优化地标对位置。该工作流程共产生了1895个地标对，平均每个病例63个。使用数字模型估计地标对的精度为0.7毫米±1.2毫米。
数据已在Zenodo上发布，链接为https://doi.org/10.5281/zenodo.14362785。使用说明可在https://github.com/deshanyang/Abdominal-DIR-QA上找到。
该数据集是首个用于腹部DIR验证的数据集。地标对的数量、精度和分布将使DIR算法能够得到强大的验证，其精度超过目前可用的水平。
