A vessel bifurcation landmark pair dataset for abdominal CT deformable image registration (DIR) validation

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2025-05-28 DOI:10.1002/mp.17907

Edward R. Criscuolo, Zhendong Zhang, Yao Hao, Deshan Yang

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引用次数: 0

Abstract

Purpose

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.

Acquisition and Validation Methods

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.

Data Format and Usage Notes

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.

Potential Applications

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.

查看原文本刊更多论文

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。

目的：可变形图像配准（DIR）是许多诊断和治疗任务的使能技术。尽管如此，DIR算法的临床应用有限，主要是由于在开发过程中缺乏质量保证的基准数据集。由于明显的器官变形和不一致的图像内容，患者腹部ct的DIRs是最具挑战性的注册场景之一。为了支持未来的算法开发，我们在这里介绍了我们的首个腹部CT DIR基准数据集，包括大量高度精确的血管分叉匹配地标对。获取和验证方法：经IRB批准，从几个公开可用的存储库以及作者所在机构获取30例患者的腹部CT图像对。每组的两组ct最初是为同一患者在不同的日期获得的。开发了一套图像处理流程，并将其应用到每对CT图像中：(1)采用深度学习模型对腹部器官进行分割，覆盖器官蒙版内的图像强度。(2)人工识别每对图像的两个ct之间的匹配图像patch。(3)在每个图像贴片对的一张图像上标记血管分叉标志。(4)对图像块进行变形配准，并将地标投影到第二幅图像上。(5)通过人工或自动化过程对地标对位置进行了细化。这个工作流程产生了1895个总地标对，或平均每个病例63个。使用数字幻影的地标对精度估计为0.7 mm±1.2 mm。数据格式和使用说明：数据发布在Zenodo网站https://doi.org/10.5281/zenodo.14362785。使用说明可在https://github.com/deshanyang/Abdominal-DIR-QA.Potential applications找到：此数据集是首个用于腹部DIR验证的数据集。里程碑对的数量、精度和分布将允许对DIR算法进行鲁棒验证，其精度超过目前可用的精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.