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Neuroscienze

使用脑部 MRI 手动分割人脉络丛

Published: December 15, 2023
doi:
10.3791/65341
, , , ,
1Department of Psychiatry,Beth Israel Deaconess Medical Center, 2Division of Translational Neuroscience,Beth Israel Deaconess Medical Center, 3Huaxi MR Research Center (HMRRC), Department of Radiology,West China Hospital of Sichuan University, 4Department of Psychiatry and Psychotherapy,Jena University Hospital, 5AI in Medical Imaging,German Center for Neurodegenerative Diseases (DZNE), 6A. A. Martinos Center for Biomedical Imaging,Massachusetts General Hospital, 7Department of Radiology,Harvard Medical School, 8Department of Psychiatry,Harvard Medical School

Summary

尽管脉络丛在大脑中起着至关重要的作用，但由于缺乏可靠的自动分割工具，这种结构的神经影像学研究很少。本方案旨在确保脉络丛的金标准手动分割，这可以为未来的神经影像学研究提供信息。

Abstract

脉络丛与神经发育和一系列脑部疾病有关。有证据表明，脉络丛对大脑成熟、免疫/炎症调节以及行为/认知功能至关重要。然而，目前的自动神经影像学分割工具在准确可靠地分割侧脑室脉络丛方面表现不佳。此外，没有现有的工具可以分割位于大脑第三和第四脑室的脉络丛。因此，需要制定一个方案来描述如何分割侧脑室、第三脑室和第四脑室中的脉络丛，以提高检查神经发育和脑部疾病中脉络丛的研究的可靠性和可复制性。该协议提供了在基于DICOM或NIFTI图像的脉络丛的3D Slicer中创建单独标记文件的详细步骤。脉络丛将使用 T1w 图像的轴向、矢状面和冠状面手动分割，确保从与心室接壤的灰质或白质结构中排除体素。将调整窗口以帮助脉络丛及其解剖边界的定位。评估准确性和可靠性的方法将作为本协议的一部分进行演示。使用手动划定对脉络丛进行金标准分割可用于开发更好、更可靠的自动分割工具，这些工具可以公开共享，以阐明脉络丛在整个生命周期和各种脑部疾病中的变化。

Introduction

脉络丛功能
脉络丛是大脑中高度血管化的结构，由开窗的毛细血管和单层脉络丛上皮细胞组成 1。脉络丛投射到外侧、第三和第四脑室并产生脑脊液 （CSF），脑脊液在神经模式2 和脑生理学 3,4 中起重要作用。脉络丛分泌神经血管物质，包含干细胞样储存库，并作为阻止有毒代谢物进入的物理屏障，去除绕过物理屏障的部分的酶屏障，以及防止外来入侵者的免疫屏障5。脉络丛调节神经发生6、突触可塑性7、炎症8、昼夜节律9、10、肠脑轴11 和认知12。此外，外周细胞因子、应激和感染（包括 SARS-CoV-2）会破坏血液脑脊液屏障13、14、15、16。因此，脉络丛-脑脊液系统是神经发育、神经回路成熟、脑稳态和修复不可或缺的一部分 17。由于免疫、炎症、代谢和酶促改变会影响大脑，研究人员正在使用神经影像学工具来评估脉络丛在整个生命周期和脑部疾病中的作用 18,19,20。然而，用于脉络丛分割的常用自动化工具（如 FreeSurfer）存在局限性，导致脉络丛分割不良。因此，迫切需要脉络丛的地面实况手动分割，可用于开发用于脉络丛分割的精确自动化工具。

脉络丛在神经发育和脑部疾病中的应用
脉络丛在脑部疾病中的作用长期以来一直被忽视，主要是因为它被认为是一个辅助角色，其作用是缓冲大脑并维持适当的盐平衡2,21。然而，脉络丛作为一种与脑部疾病相关的结构而受到关注，例如疼痛综合征22、SARS-CoV-216、23、24、神经发育2 和脑部疾病19，这表明在行为障碍的发展中具有跨诊断作用。在神经发育障碍中，脉络丛囊肿与发育迟缓、注意力缺陷/多动障碍 （ADHD） 或自闭症谱系障碍 （ASD） 的风险增加有关25,26。此外，发现 ASD27 患者的侧脑室脉络丛体积增加。在脑部疾病中，脉络丛异常自 1921 年以来一直在精神障碍中被描述 28,29。先前的研究已经使用 FreeSurfer 分割在精神障碍患者的大样本中确定了脉络丛增大，与他们的一级亲属和对照组相比 19.在精神病临床高危人群的大样本中使用手动分割的脉络丛体积重复了这些发现，发现与健康对照组相比，这些患者的脉络丛体积更大30。越来越多的研究表明脉络丛肿大在复杂区域疼痛综合征22、中风31、多发性硬化症 20,32、阿尔茨海默氏症33,34 和抑郁症35 中，其中一些证明了外周和大脑免疫/炎症活动之间的联系。这些神经影像学研究很有希望;然而，FreeSurfer21 对侧脑室脉络丛分割不良限制了自动脉络丛体积估计的可信度。因此，多发性硬化症20,32、抑郁症35、阿尔茨海默氏症34 和早期精神病36 的研究已经开始手动分割侧脑室脉络丛，但目前没有关于如何做到这一点的指南，也没有关于分割第三和第四脑室脉络丛的指南。

常见的分割工具排除脉络丛
大脑分割管道，如 FreeSurfer37,38,39、FMRIB 软件库 （FSL）40、SLANT41 和 FastSurfer（由合著者 Martin Reuter 开发）42,43，使用基于图谱 （FSL）、基于图谱和表面的 （FreeSurfer） 以及深度学习分割范式（SLANT 和 FastSurfer）准确可靠地分割皮层和皮层下结构。其中一些方法的缺点包括处理速度、对不同扫描仪的有限泛化、场强和体素大小 37,44，以及在标准图集空间中强制对齐标签图。然而，脉络丛分割的能力以及与高分辨率 MRI 的兼容性仅由 FreeSurfer 和 FastSurfer 解决。FastSurfer 背后的神经网络是在 FreeSurfer 脉络丛标签上训练的，因此它们继承了 FreeSurfer 之前讨论的可靠性和覆盖范围限制，第三和第四脑室被忽略21。目前高分辨率 MRI 也存在局限性，但 FreeSurfer 的高分辨率流45 和 FastSurferVINN43 可用于处理此问题。

当前的脉络丛分割工具
脉络丛只有一种免费可用的分割工具，但分割精度有限。准确的脉络丛分割可能受到多种因素的影响，包括 （1） 脉络丛位置的变异性（空间上非平稳的），这是由于其在心室内的位置，（2） 体素强度、对比度、分辨率（结构内异质性）的差异，由于细胞异质性、动态脉络丛功能、病理变化或部分体积效应，（3） 影响脉络丛大小的年龄或病理学相关的心室大小差异， （4）靠近相邻的皮质下结构（海马体、杏仁核、尾状核和小脑），这些结构也很难分割。鉴于这些挑战，FreeSurfer 分割经常低估或高估、错误标记或忽略脉络丛。

最近的三篇出版物解决了使用高斯混合模型 （GMM）46、轴向 MLP47 和基于 U-Net 的深度学习方法48 进行可靠脉络丛分割的差距。每个模型都使用最多 150 名受试者的私有、手动标记数据集进行训练和评估，这些受试者的扫描仪、部位、人口统计学和疾病的多样性有限。虽然这些出版物 46,48,49 比 FreeSurfer 的脉络丛分割取得了显着改进——有时将预测和地面实况的交叉点增加了一倍，但这两种方法都没有 （1） 在高分辨率 MRI 中得到验证，（2） 具有专门的泛化和可靠性分析，（3） 具有大型代表性的训练和测试数据集，（4） 专门解决或分析脉络丛分割挑战，例如部分体积效应，或 （5） 作为即用型工具公开提供。因此，目前脉络丛分割的“金标准”是手动追踪，例如，使用 3D Slicer50 或 ITK-SNAP51，这在以前没有描述过，对于希望检查脉络丛在其研究中的作用的研究人员来说，这是一个重大挑战。之所以选择3D Slicer进行手动分割，是因为作者熟悉该软件，并且因为它为用户提供了基于不同方法的各种工具，这些工具可以组合在一起以获得所需的结果。可以使用其他工具，例如主要面向图像分割的ITK-SNAP，一旦掌握了该工具，用户就可以获得良好的效果。此外，作者还进行了一项病例对照研究，证明了他们使用 3D Slicer30 的手动分割技术的高准确性和可靠性，本文描述了具体方法。

Protocol

本协议已获得贝斯以色列女执事医疗中心机构审查委员会的批准。使用脑部 MRI 扫描没有伪影或运动的健康受试者进行该协议演示，并获得书面知情同意书。使用带有 32 通道头线圈的 3.0 T MRI 扫描仪（参见 材料表）采集分辨率为 1 mm x 1 mm x 1.2 mm 的 3D-T1 图像。使用 MP-RAGE ASSET 序列，视场为 256 x 256，TR/TE/TI=7.38/3.06/400 ms，翻转角度为 11 度。 1. 将脑部 MRI 导入 3D ?…

Representative Results

所提出的方法对侧脑室脉络丛进行了迭代改进，涉及对 169 名健康对照和 340 名临床精神病高风险患者的队列进行了广泛测试30。使用上述技术，作者在DC = 0.89，avgHD = 3.27 mm3和单评分者ICC = 0.9730的情况下获得了较高的评分者内部准确性和可靠性，证明了本文所述方案的强度。 处理质量控制问题和 3D 切片器设置在开始分割过程之前，有?…

Discussion

协议的关键步骤
在实施该协议时，需要特别注意三个关键步骤。首先，检查MR图像的质量和对比度是确保准确分割的关键。如果图像质量太差，或者对比度太低或太高，都可能导致脉络丛的描绘不准确。可以通过查看图像的灰度值或校准值来增强灰质核和灰质之间的对比度来调整图像的对比度。其次，评估者需要熟悉脉络丛的解剖结构并接受过专门培训。如果评估者不熟悉脉络丛?…

Divulgazioni

The authors have nothing to disclose.

Acknowledgements

这项工作得到了美国国家心理健康研究所奖 R01 MH131586（授予 PL 和 MR）、R01 MH078113（授予 MK）和 Sydney R Baer Jr 基金会资助（授予 PL）的支持。

Materials

3D Slicer 3D Slicer https://www.slicer.org/ A free, open source software for visualization, processing, segmentation, registration, and analysis of medical, biomedical, and other 3D images and meshes; and planning and navigating image-guided procedures.
FreeSurfer FreeSurfer https://surfer.nmr.mgh.harvard.edu/ An open source neuroimaging toolkit for processing, analyzing, and visualizing human brain MR images
ITK-SNAP ITK-SNAP http://www.itksnap.org/pmwiki/pmwiki.php A free, open-source, multi-platform software application used to segment structures in 3D and 4D biomedical images. 
Monai Package Monai Consortium https://docs.monai.io/en/stable/metrics.html Use for Dice Coefficient and DeepMind average Surface Distance. 
MRI scanner GE Discovery MR750 
Psych Package R-Project https://cran.r-project.org/web/packages/psych/index.html A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology.
R Software R-Project https://www.r-project.org/ R is a free software environment for statistical computing and graphics. 
RStudio Posit https://posit.co/ An RStudio integrated development environment (IDE) is a set of tools built to help you be more productive with R and Python. 
Windows or Apple OS Desktop or Laptop Any company n/a Needed for running the software used in this protocol. 
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Choroid PlexusManual SegmentationBrain MRILateral VentricleThird VentricleFourth VentricleNeuroimagingNeurodevelopmentBrain Disorders3D SlicerT1-weighted ImagesAutomated Segmentation
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Bannai, D., Cao, Y., Keshavan, M., Reuter, M., Lizano, P. Manual Segmentation of the Human Choroid Plexus Using Brain MRI. J. Vis. Exp. (202), e65341, doi:10.3791/65341 (2023).
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