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首页> 美国卫生研究院文献>Diagnostics >Deep Learning-Based Segmentation to Establish East Asian Normative Volumes Using Multisite Structural MRI
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Deep Learning-Based Segmentation to Establish East Asian Normative Volumes Using Multisite Structural MRI

机译：基于深度学习的分割建立了利用多路面结构MRI建立了东亚规范体积

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Normative brain magnetic resonance imaging (MRI) is essential to interpret the state of an individual’s brain health. However, a normative study is often expensive for small research groups. Although several attempts have been made to establish brain MRI norms, the focus has been limited to certain age ranges. This study aimed to establish East Asian normative brain data using multi-site MRI and determine the robustness of these data for clinical research. Normative MRI was gathered covering a wide range of cognitively normal East Asian populations (age: 18–96 years) from two open sources and three research sites. Eight sub-regional volumes were extracted in the left and right hemispheres using an in-house deep learning-based tool. Repeated measure consistency and multicenter reliability were determined using intraclass correlation coefficients and compared to a widely used tool, FreeSurfer. Our results showed highly consistent outcomes with high reliability across sites. Our method outperformed FreeSurfer in repeated measure consistency for most structures and multicenter reliability for all structures. The normative MRI we constructed was able to identify sub-regional differences in mild cognitive impairments and dementia after covariate adjustments. Our investigation suggests it is possible to provide a sound normative reference for neurodegenerative or aging research.
机译：规范性脑磁共振成像（MRI）对于解释个人的脑部健康状况至关重要。然而，规范性研究通常对小型研究组昂贵。虽然已经进行了几次尝试建立脑MRI规范，但重点仅限于某些年龄范围。本研究旨在使用多网站MRI建立东亚规范性脑数据，并确定这些数据的临床研究的鲁棒性。从两个开放来源和三个研究网站覆盖各种认知常规东亚人口（年龄：18-96岁）的规范性MRI。使用内部深度学习的工具在左右半球中提取八个次区域体积。重复测量一致性和多中心可靠性使用跨型相关系数确定并与广泛使用的工具进行比较，FreeSurfer相比。我们的结果表明，跨地网站的可靠性高度一致。我们的方法在重复测量的一致性中表现优于所有结构的大多数结构和多中心可靠性。我们构建的规范MRI能够在协变量调整后确定轻度认知障碍和痴呆症的次区域差异。我们的调查表明，可以为神经变性或老化研究提供合理的规范参考。

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