Name: 作者聚焦：弥合解剖学差距并为算法研究奠定基础
Uploaded: 2023-12-15T12:40:00
Description: Watch this Scientific Journal Video about Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies at JoVE.com

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。

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 ?...

加强脑部疾病的神经影像学分析

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?...

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。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>3D Slicer</td>
			<td>3D Slicer</td>
			<td>https://www.slicer.org/</td>
			<td>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.</td>
		</tr>
		<tr>
			<td>FreeSurfer</td>
			<td>FreeSurfer</td>
			<td>https://surfer.nmr.mgh.harvard.edu/</td>
			<td>An open source neuroimaging toolkit for processing, analyzing, and visualizing human brain MR images</td>
		</tr>
		<tr>
			<td>ITK-SNAP</td>
			<td>ITK-SNAP</td>
			<td>http://www.itksnap.org/pmwiki/pmwiki.php</td>
			<td>A free, open-source, multi-platform software application used to segment structures in 3D and 4D biomedical images.&#160;</td>
		</tr>
		<tr>
			<td>Monai Package</td>
			<td>Monai Consortium</td>
			<td>https://docs.monai.io/en/stable/metrics.html</td>
			<td>Use for Dice Coefficient and DeepMind average Surface Distance.&#160;</td>
		</tr>
		<tr>
			<td>MRI scanner</td>
			<td>GE</td>
			<td>Discovery MR750&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>Psych Package</td>
			<td>R-Project</td>
			<td>https://cran.r-project.org/web/packages/psych/index.html</td>
			<td>A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology.</td>
		</tr>
		<tr>
			<td>R Software</td>
			<td>R-Project</td>
			<td>https://www.r-project.org/</td>
			<td>R is a free software environment for statistical computing and graphics.&#160;</td>
		</tr>
		<tr>
			<td>RStudio</td>
			<td>Posit</td>
			<td>https://posit.co/</td>
			<td>An RStudio integrated development environment (IDE) is a set of tools built to help you be more productive with R and Python.&#160;</td>
		</tr>
		<tr>
			<td>Windows or Apple OS Desktop or Laptop</td>
			<td>Any company</td>
			<td>n/a</td>
			<td>Needed for running the software used in this protocol.&#160;</td>
		</tr>
	</tbody>
</table>

manual segmentation of the human choroid plexus using brain mri

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。

Choroid plexus function 
The choroid plexus is a highly vascularized structure in the brain consisting of fenestrated capillaries and a monolayer of choroid plexus epithelial cells1. The choroid plexus projects into the lateral, third, and fourth cerebral ventricles and produces cerebrospinal fluid (CSF), which plays an important role in neural patterning2 and brain physiology3,4. The choroid plexus secretes neurovascular substances, encompasses a stem-cell like repository, and acts as a physical barrier to impede the entrance of toxic metabolites, an enzymatic barrier to remove moieties that circumvent the physical barrier, and an immunological barrier to protect against foreign invaders5. The choroid plexus modulates neurogenesis6, synaptic plasticity7, inflammation8, circadian rhythm9,10, gut brain-axis11, and cognition12. Moreover, peripheral cytokines, stress, and infection (including SARS-CoV-2) can disrupt the blood-CSF barrier13,14,15,16. Thus, the choroid plexus-CSF system is integral for neurodevelopment, neurocircuit maturation, brain homeostasis, and repair17. Since immune, inflammatory, metabolic, and enzymatic alterations impact the brain, researchers are using neuroimaging tools to assess the role of the choroid plexus across the lifespan and in brain disorders18,19,20. However, limitations exist in commonly used automated tools for choroid plexus segmentation, such as FreeSurfer, which result in the choroid plexus being poorly segmented. Thus, there is a critical need for ground truth manual segmentation of the choroid plexus that can be used to develop an accurate automated tool for choroid plexus segmentation.
Choroid plexus in neurodevelopment and brain disorders 
The role of the choroid plexus in brain disorders has long been neglected, mainly because it was regarded as a supporting player whose role was to cushion the brain and maintain a proper salt balance2,21. However, the choroid plexus has gained attention as a structure linked to brain disorders such as pain syndromes22, SARS-CoV-216,23,24, neurodevelopmental2, and brain disorders19, suggesting a transdiagnostic effect in the development of behavioral disorders. In neurodevelopmental disorders, choroid plexus cysts were associated with an increased risk of developmental delay, attention-deficit/hyperactivity disorder (ADHD), or autism spectrum disorder (ASD)25,26. Additionally, lateral ventricle choroid plexus volume was found to be increased in patients with ASD27. In brain disorders,choroid plexus abnormalities have been described since 1921 in psychotic disorders28,29. Previous studies have identified choroid plexus enlargement using FreeSurfer segmentation in a large sample of patients with psychotic disorders compared to both their first-degree relatives and controls19. These findings were replicated using manually segmented choroid plexus volume in a large sample of clinical high-risk for psychosis population and found that these patients had larger choroid plexus volume compared to healthy controls30. There are a growing number of studies demonstrating choroid plexus enlargement in complex regional pain syndrome22, stroke31, multiple sclerosis20,32, Alzheimer&#39;s33,34, and depression35, with some demonstrating a link between peripheral and brain immune/inflammatory activity. These neuroimaging studies are promising; however, poor lateral ventricle choroid plexus segmentation by FreeSurfer21 limits the trustworthiness of automated choroid plexus volume estimation. As a result, studies in multiple sclerosis20,32, depression35, Alzheimer&#39;s34, and early psychosis36 have begun manually segmenting the lateral ventricle choroid plexus, but there are no current guidelines for how to do this, nor is their guidance on segmenting the third and fourth ventricle choroid plexus.
Common segmentation tools exclude the choroid plexus 
Brain segmentation pipelines such as FreeSurfer37,38,39, FMRIB Software Library (FSL)40, SLANT41, and FastSurfer (developed by the co-author Martin Reuter)42,43, accurately and reliably segment cortical and subcortical structures employing atlas-based (FSL), atlas- and surface-based (FreeSurfer), and deep learning segmentation paradigms (SLANT and FastSurfer). Weaknesses of some of these approaches include processing speed, limited generalization to different scanners, field strengths and voxel sizes37,44, and forced alignment of the label map in a standard atlas space. However, the capability to segment the choroid plexus and the compatibility with high-resolution MRI is only addressed by FreeSurfer and FastSurfer. The neural networks behind FastSurfer, are trained on FreeSurfer choroid plexus labels, so they inherit FreeSurfer&#39;s previously discussed reliability and coverage limitations, with the third and fourth ventricles being ignored21. Current limitations for high-resolution MRI also exist, but FreeSurfer&#39;s high-resolution stream45 and FastSurferVINN43 can be used to handle this issue.
Current choroid plexus segmentation tools 
There is only one freely available segmentation tool for the choroid plexus, but segmentation accuracy is limited. Accurate choroid plexus segmentation can be impacted by a variety of factors, including (1) variability in choroid plexus location (spatially non-stationary) due to its location within the ventricles, (2) differences in voxel intensity, contrast, resolution (within-structure heterogeneity) due to cellular heterogeneity, dynamic choroid plexus function, pathological changes, or partial volume effects, (3) age- or pathology-related ventricular size differences impacting choroid plexus size, and (4) proximity to adjacent subcortical structures (hippocampus, amygdala, caudate, and cerebellum), which are also difficult to segment. Given these challenges, FreeSurfer segmentations often under or over-estimate, mislabel or ignore the choroid plexus.
Three recent publications addressed the gap of reliable choroid plexus segmentation with a Gaussian Mixture Model (GMM)46, an Axial-MLP47, and U-Net-based deep learning approaches48. Each model was trained and evaluated using private, manually labeled datasets of at most 150 subjects with a limited diversity of scanners, sites, demographics, and disorders. While these publications46,48,49 achieved significant improvements over FreeSurfer&#39;s choroid plexus segmentation - sometimes doubling the intersection of prediction and ground truth, neither method is (1) validated in high-resolution MRI, (2) has dedicated generalization and reliability analyses, (3) features large representative training and testing datasets, (4) specifically addresses or analyzes choroid plexus segmentation challenges such as partial volume effects, or (5) is publicly available as a ready-to-use tool. Thus, the current &#34;gold standard&#34; for choroid plexus segmentation is manual tracing, e.g., using 3D Slicer50 or ITK-SNAP51, which has not been previously described and has been a major challenge for researchers wishing to examine the role of the choroid plexus in their studies. 3D Slicer was chosen for manual segmentation due to the author&#39;s familiarity with the software and because it provides the user with various tools based on different approaches that can be combined to obtain the desired result. Other tools can be used, such as ITK-SNAP, which is primarily oriented on image segmentation, and once the tool is mastered, good results can be obtained by the user. Additionally, the authors have conducted a case-control study demonstrating the high accuracy and reliability of their manual segmentation technique using 3D Slicer30, and that specific methodology is described herein.

作者聚焦：弥合解剖学差距并为算法研究奠定基础 

使用脑部 MRI 手动分割人脉络丛

We aim to establish an accessible and standardized protocol for manually segmenting the choroid plexus within the lateral third and fourth ventricles of the brain. We hope to address any common questions or problems that can arise when manually segmenting the choroid plexus, especially considering the complex nature of its location within the brain. Developing this protocol will lead to the creation of a large enough data set consisting of ground truth segmentation maps that we can then use to train and develop algorithms that can accurately and automatically segment the choroid plexus.In the future, this will lead to studies that investigate the role of the choroid plexus within various neurologic and neuropsychiatric disorders.

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?...

Watch this Scientific Journal Video about Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies at JoVE.com

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。

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for ...

我们的目标是建立一个可访问的标准化协议，用于手动分割大脑外侧第三和第四脑室内的脉络丛。我们希望解决手动分割脉络丛时可能出现的任何常见问题或问题，特别是考虑到其在大脑中位置的复杂性。开发该协议将导致创建一个足够大的数据集，该数据集由地面实况分割图组成，然后我们可以使用它来训练和开发能够准确自动分割脉络丛的算法。将来，这将导致研究脉络丛在各种神经和神经精神疾病中的作用。

manual-segmentation-of-the-human-choroid-plexus-using-brain-mri

Research

JoVE Journal

Neuroscience

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for brain maturation, immune/ inflammatory regulation, and behavioral/cognitive functioning. However, current automated neuroimaging segmentation tools are poor at accurately and reliably segmenting the lateral ventricle choroid plexus. Furthermore, there is no existing tool that segments the choroid plexus located in the third and fourth ventricles of the brain. Thus, a protocol delineating how to segment the choroid plexus in the lateral, third, and fourth ventricle is needed to increase the reliability and replicability of studies examining the choroid plexus in neurodevelopmental and brain disorders. This protocol provides detailed steps to create separately labeled files in 3D Slicer for the choroid plexus based on DICOM or NIFTI images. The choroid plexus will be manually segmented using the axial, sagittal, and coronal planes of T1w images making sure to exclude voxels from gray or white matter structures bordering the ventricles. Windowing will be adjusted to assist in the localization of the choroid plexus and its anatomical boundaries. Methods for assessing accuracy and reliability will be demonstrated as part of this protocol. Gold standard segmentation of the choroid plexus using manual delineations can be used to develop better and more reliable automated segmentation tools that can be openly shared to elucidate changes in the choroid plexus across the lifespan and within various brain disorders.

Despite the crucial role of the choroid plexus in the brain, neuroimaging studies of this structure are scarce due to the lack of reliable automated segmentation tools. The present protocol aims to ensure gold-standard manual segmentation of the choroid plexus that can inform future neuroimaging studies.

科研

神经科学

Segmentation of the Choroid Plexus Using Manual Delineations

To begin, import digital imaging and communications in medicine or DICOM data by clicking on the DCM button located at the upper left corner of the toolbar and clicking the import DICOM files button. To adjust the brightness of an image, click adjust window and level of volume or left click on the image and drag the mouse up or down. Then left click on the image slice and move the mouse to the left or right to adjust the contrast.Refer to the deep gray matter nuclei or the contrast scale bars signal intensity and ensure that it remains consistent throughout the segmentation to choose the suitable contrast for the choroid plexus. To start the segmentation of the lateral third and fourth ventricle choroid plexus, select the segment editor from the module's dropdown menu. Now click on the dropdown menu for segmentation to select different segmentations and rename the selected one.Use the master volume dropdown and pick the NIfTI or DICOM sets that need editing. Click the add button two times to include two segments for the lateral ventricle choroid plexus. Double click on their titles and rename them as right lateral ventricle choroid plexus and left lateral ventricle choroid plexus.Click on the add button once more to introduce segments for the choroid plexus of the third and fourth ventricles. Afterward, rename them as the third ventricle choroid plexus and the fourth ventricle choroid plexus respectively. Begin with the lateral ventricle choroid plexus in the axial plane and ensure to position images based on the bicommissural line.Use the trigonum collaterale as a reference point to locate the lateral ventricle choroid plexus. To start manual segmentation, select either the paint or draw tool located in the effects section of the segment editor. While using the paint tool, select the desired brush diameter for painting.Use the red nucleus level to determine when to stop segmenting the choroid plexus in the lateral ventricles. To locate the third ventricle choroid plexus, commence in the sagittal plane, and use the foramen of Monro, fornix, corpus callosum, thalamus, and internal cerebral vein as landmarks. Similarly, for the fourth ventricle choroid plexus, begin in the sagittal plane and use the superior cerebellar peduncle, pons and medulla as reference points to pinpoint the choroid plexus in the fourth ventricle.From the modules dropdown menu, navigate to quantification and select segment statistics. Then under the input section, select the newly created segmentation map for quantification under the segmentation tool and select the MRI volume from the scaler volume. For the output table, select the table option under output and hit apply.A table displaying the choroid plexus volume in different units will appear. Click on the save button located in the top left part of the toolbar to store the generated files. Preserve the segmentation files in the formats nrrd, nii.gz, or tsv.