论文标题
用于神经影像数据集的协调基准测试工具
Harmonization Benchmarking Tool for Neuroimaging Datasets
论文作者
论文摘要
大型多站点研究的主要数据预处理步骤是通过协调数据来处理站点效应，生成一个数据集，该数据集可实现更强大的分析和更强大的算法。有各种各样的数据协调技术，但是很少有工具简化了协调数据，跨技术进行比较以及基准测试新技术的过程。在本文中，我们引入了协调基准测试工具（HABET），这是一种开源工具，用于生成统一的图像并评估不同统一算法的性能。为了证明HABET的能力，我们使用两种不同的方法来协调从青少年大脑和认知发展（ABCD）研究中的扩散MRI图像，我们比较了它们的性能。
A major data pre-processing step for large, multi-site studies is to handle site effects by harmonizing data, generating a dataset that enables more powerful analyses and more robust algorithms. There is a wide variety of data harmonization techniques, but there are few tools that streamline the process of harmonizing data, comparing across techniques, and benchmarking new techniques. In this paper, we introduce HArmonization BEnchmarking Tool (HABET), an open source tool for generating harmonized images and evaluating the performance of different harmonization algorithms. To demonstrate the capabilities of HABET, we harmonize diffusion MRI images from the Adolescent Brain and Cognitive Development (ABCD) study using two different approaches, and we compare their performance.
