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一个用于研究人类舌肌组织的带注释的多部位多对比磁共振成像数据集。

An Annotated Multi-Site and Multi-Contrast Magnetic Resonance Imaging Dataset for the study of the Human Tongue Musculature.

作者信息

Ribeiro Fernanda L, Zhu Xiangyun, Ye Xincheng, Tu Sicong, Ngo Shyuan T, Henderson Robert D, Steyn Frederik J, Kiernan Matthew C, Barth Markus, Bollmann Steffen, Shaw Thomas B

机构信息

School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Queensland, Australia.

Griffith School of Medicine and Dentistry, Brisbane, Queensland, Australia.

出版信息

Sci Data. 2025 May 14;12(1):790. doi: 10.1038/s41597-025-05092-8.

Abstract

This dataset provides the first annotated, openly available MRI-based imaging dataset for investigations of tongue musculature, including multi-contrast and multi-site MRI data from non-disease participants. The present dataset includes 47 participants collated from three studies: BeLong (four participants; T2-weighted images), EATT4MND (19 participants; T2-weighted images), and BMC (24 participants; T1-weighted images). We provide manually corrected segmentations of five key tongue muscles: the superior longitudinal, combined transverse/vertical, genioglossus, and inferior longitudinal muscles. Other phenotypic measures, including age, sex, weight, height, and tongue muscle volume, are also available for use. This dataset will benefit researchers across domains interested in the structure and function of the tongue in health and disease. For instance, researchers can use this data to train new machine learning models for tongue segmentation, which can be leveraged for segmentation and tracking of different tongue muscles engaged in speech formation in health and disease. Altogether, this dataset provides the means to the scientific community for investigation of the intricate tongue musculature and its role in physiological processes and speech production.

摘要

该数据集提供了首个经过注释且可公开获取的基于MRI的舌肌成像数据集,用于舌肌研究,包括来自非疾病参与者的多对比度和多部位MRI数据。本数据集包含从三项研究中整理出的47名参与者:BeLong(4名参与者;T2加权图像)、EATT4MND(19名参与者;T2加权图像)和BMC(24名参与者;T1加权图像)。我们提供了对五块关键舌肌的手动校正分割:上纵肌、横/垂直联合肌、颏舌肌和下纵肌。其他表型测量数据,包括年龄、性别、体重、身高和舌肌体积,也可供使用。该数据集将使对健康和疾病状态下舌的结构与功能感兴趣的各领域研究人员受益。例如,研究人员可以使用这些数据训练用于舌分割的新机器学习模型,这些模型可用于在健康和疾病状态下对参与语音形成的不同舌肌进行分割和跟踪。总之,该数据集为科学界提供了研究复杂舌肌及其在生理过程和语音产生中作用的手段。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4546/12078697/617218abe2b9/41597_2025_5092_Fig1_HTML.jpg

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