Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada.
School of Biomedical Engineering, Western University, London, Canada.
Sci Data. 2023 Jul 12;10(1):449. doi: 10.1038/s41597-023-02330-9.
Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging datasets and templates. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (N = 132 subjects for a total of 15,232 fiducials) and 14 brain templates (4,288 fiducials), totalling more than 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1 - 2 mm (using more than 45,000 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure allowing for facile incorporation into neuroimaging analysis pipelines.
用于可重复、定量评估大脑对应关系的工具一直很有限。我们之前已经验证了基于解剖学标记点（AFID）的位置协议，用于以毫米（mm）精度进行基于点的图像配准评估。在这个数据描述符中，我们发布了一些最常用的结构磁共振成像数据集和模板的经过精心整理的 AFID 位置。我们准确放置的发布允许快速进行图像配准的质量控制，教授神经解剖学，并应用于疾病诊断和手术靶向等临床应用。我们在四个数据集（共 132 个个体，总共 15232 个标记点）和 14 个脑模板（4288 个标记点）上发布了位置，总共有超过 300 个人类评分者的注释小时数。我们还验证了发布的位置的人类评分者的准确性在 1 - 2mm 以内（使用超过 45000 个欧几里得距离），与之前的研究一致。我们的数据符合脑成像数据结构，便于纳入神经影像学分析管道。
