首个用于腹部CT形变配准验证的血管分叉标志点数据集，助力算法精准度评估。
A Vessel Bifurcation Landmark Pair Dataset for Abdominal CT Deformable Image Registration (DIR) Validation
- 基于深度学习分割器官后，人工标注血管分叉点并校准配准结果。
- 共构建1895对标志点，平均每例63对，误差0.7±1.2mm。
- 适合研究医学图像配准、需高精度验证的算法开发者使用。
形变图像配准（DIR）在诊断与治疗中具有重要作用，但因缺乏用于质量保障的基准数据集，临床应用受限。本文首次发布腹部CT DIR验证基准数据集，包含大量高精度血管分叉标志点对。从多个公开数据库及作者机构获取30例患者的双期腹部CT图像，每对图像来自同一患者不同日期扫描。通过深度学习模型分割腹部器官并掩码图像强度；手动识别匹配图像块；在每对图像块中于单幅图像上标注血管分叉点；将图像块进行形变配准并将标志点投影至另一图像；最终通过人工或自动方法优化标志点位置。共生成1895对标志点，平均每例63对。使用数字体模估计的标志点精度为0.7±1.2mm。数据已发布于Zenodo（https://doi.org/10.5281/zenodo.14362785），使用说明见GitHub（https://github.com/deshanyang/Abdominal-DIR-QA）。该数据集为腹部DIR验证首例，其数量、精度与分布可支持超越当前水平的算法鲁棒性验证。
原文摘要 · Abstract (English)
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.
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