Name: Segmentation of the Choroid Plexus Using Manual Delineations
Uploaded: 2023-12-15T12:40:00
Description: Watch this Scientific Journal Video about Segmentation of the Choroid Plexus Using Manual Delineations at JoVE.com

segmentation of the choroid plexus using manual delineations

加强脑部疾病的神经影像学分析

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.

Watch this Scientific Journal Video about Segmentation of the Choroid Plexus Using Manual Delineations at JoVE.com

Segmentation of the Choroid Plexus Using Manual Delineations  

使用手动划定对脉络丛进行分割

首先，通过单击工具栏左上角的 DCM 按钮并单击导入 DICOM 文件按钮，导入医学或 DICOM 数据中的数字成像和通信。要调整图像的亮度，请单击调整窗口和音量级别，或左键单击图像并向上或向下拖动鼠标。然后左键单击图像切片并向左或向右移动鼠标以调整对比度。参考深灰质核或对比度比例尺信号强度，并确保在整个分割过程中保持一致，为脉络丛选择合适的对比度。要开始分割外侧第三和第四脑室脉络丛，请从模块的下拉菜单中选择分段编辑器。现在单击细分的下拉菜单以选择不同的细分并重命名所选的细分。使用主音量下拉列表并选择需要编辑的 NIfTI 或 DICOM 集。单击添加按钮两次以包括侧脑室脉络丛的两个节段。双击它们的标题并将它们重命名为右脑室脉络丛和左脑室脉络丛。再次单击添加按钮以介绍第三和第四脑室脉络丛的节段。之后，将它们分别重命名为第三室脉络丛和第四室脉络丛。从轴向平面的侧脑室脉络丛开始，并确保根据双线定位图像。使用三角侧支作为参考点来定位侧脑室脉络丛。要开始手动分割，请选择位于片段编辑器的效果部分中的绘画或绘图工具。使用绘画工具时，选择所需的画笔直径进行绘画。使用红核水平确定何时停止分割侧脑室中的脉络丛。要定位第三室脉络丛，从矢状面开始，以门罗孔、穹窿孔、胼胝体、丘脑和大脑内静脉为标志。同样，对于第四脑室脉络丛，从矢状面开始，使用小脑上蒂、脑桥和延髓作为参考点，以精确定位第四脑室中的脉络丛。从模块下拉菜单中，导航到量化并选择区段统计。然后在输入部分下，在分割工具下选择新创建的分割图进行定量，然后从定标器体积中选择 MRI 体积。对于输出表，选择输出下的表选项，然后点击应用。将出现一个以不同单位显示脉络丛体积的表格。单击工具栏左上角的保存按钮以存储生成的文件。以 nrrd、nii 格式保留分段文件。GZ 或 TSV。

Research

JoVE Journal

Neuroscience

Segmentation of the Choroid Plexus Using Manual Delineations

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 ...