Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Deepthi Bannai; Yuan Cao; Matcheri Keshavan; Martin Reuter; Paulo Lizano

doi:10.3791/65341

JoVE Journal > Neuroscience

신경과학

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published: December 15, 2023

doi:

10.3791/65341

Deepthi Bannai*^1,2, Yuan Cao*^3,4, Matcheri Keshavan^1,2, Martin Reuter^5,6,7, Paulo Lizano^1,2,8

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

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.

Abstract

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.

Introduction

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'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'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'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'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'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 "gold standard" 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'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.

Protocol

The present protocol was approved by the Institutional Review Board at Beth Israel Deaconess Medical Center. A healthy subject with a brain MRI scan that was free of artifacts or movement was used for this protocol demonstration, and written informed consent was obtained. A 3.0 T MRI scanner with a 32-channel head coil (see Table of Materials) was used to acquire 3D-T1 images with a 1 mm x 1 mm x 1.2 mm resolution. The MP-RAGE ASSET sequence with a 256 x 256 field of view, TR/TE/TI=7.38/3.06/400 ms, and an 11-degree flip angle was used.

1. Importing brain MRI to 3D Slicer

NOTE: 3D Slicer provides documentation related to its user interface.

Prepare the brain MRI DICOM (Digital Imaging and Communications in Medicine) or NIFTI (Neuroimaging Informatics Technology Initiative) files for importing into 3D Slicer.
Import DICOM data by clicking the DCM button in the upper left corner of the toolbar. Then click the Import DICOM files button to import DICOM format data.
If the MRI data is in the NIFTI format, import it by clicking the DATA button in the upper left corner of the toolbar. In the pop-up dialog, select Choose directory to Add to batch import NIFTI data in a folder or select Choose File(s) to Add to import specific NIFTI files. Then, click the OK button to proceed with data upload to 3D Slicer.
After importation, MRI data will appear in the window on the right displaying axial, sagittal, and coronal planes.
Change the layout of the windows by navigating to Layouts and selecting a specific layout. This can be found by either clicking on the Layouts module image in 3D Slicer's toolbar itself or via View > Layouts from the application menu.

2. Downloading DICOM from sample data in 3D Slicer

Click the Download sample data button in the opening screen of Welcome to Slicer section. Then select the MRHead button, and it will start the download process, which may take a few minutes.
Ensure that the brain MRI data with axial, sagittal, and coronal planes are shown on the right window.

3. Quality control and adjusting the MRI image

Determine qualitatively the image quality and the presence of artifacts due to head motion or scanning issues by critically reviewing each MRI slice.
Zoom the image slice by right-clicking and moving the mouse up or down to zoom out or in, respectively.
To move the image slice around, left-click the image, press and hold the Shift key, and drag the mouse around.
Adjusting the image brightness can help with viewing the choroid plexus. To do this, either click on Adjust window/level of volume in the toolbar or left-click the image and move the mouse up or down to raise or lower the brightness, respectively.
Adjusting contrast can additionally help with finding the choroid plexus. Left-click on the image slice and move the mouse to the left or right to increase or decrease the contrast, respectively. To determine the appropriate contrast for the choroid plexus, use the deep gray matter nuclei (central masses of gray matter arrayed around the lateral and third ventricles) or the signal intensity shown in the contrast scale bar.
Once the preferred contrast is selected, maintain the same contrast throughout segmentation and do not adjust for potential variations in supra- and infra-tentorial regions.

4. Creating the manual segments of the choroid plexus

To begin segmentation of the lateral, third, and fourth ventricle choroid plexus, create the segmentation files in the Segment Editor module. To navigate there, click either on the Segment Editor in the toolbar or go to the Modules: drop-down menu and select Segment Editor.
Click on the drop-down menu for segmentation to select different segmentations (if multiple segmentations were created) and rename the currently selected segmentation.
Use the Master Volume drop-down to choose which NIFTI or DICOM sets need editing. Only when the volume file is selected, the user can start segmenting/editing.
Click the Add button twice to add two segments for the lateral ventricle choroid plexus. To rename these, double-click on the name and change them to Right lateral ventricle choroid plexus and Left lateral ventricle choroid plexus.
Click the Add button again to add segments for the third and fourth ventricle choroid plexus, and rename them to "3^rd ventricle choroid plexus" and "4^th ventricle choroid plexus".

5. Viewing different slices and segmentations

Before editing, perform a background study to know how to move between layouts in the viewing window and how to change the view or opacity of the segmentations.
At the top of the viewing window and to the left of the slice slider, click on the pin icon. This will open a drop-down menu, which may vary depending on the specific layout the window is in.
NOTE: Utilizing different layouts can be helpful when segmenting the choroid plexus since its structure can vary between individuals. For example, the 'Conventional' layout allows the user to simultaneously view all three slices and a 3D view of the scene. Choosing 'Red/Yellow/Green slice only' gives the user a close-up view of the 2D slice to allow for a more precise segmentation of the choroid plexus.

6. Delineating lateral ventricle choroid plexus ROIs

NOTE: Image registration to a template is not necessary for manual segmentation.

For the lateral ventricle choroid plexus, begin in the axial plane ensuring that the images are positioned based on the bicommissural line. Then use the trigonum collaterale as a reference point for locating the lateral ventricle choroid plexus.
1. Once edits have been made in the axial plane, move to the remaining views (sagittal and coronal) to ensure that the manual segmentation of the lateral ventricle choroid plexus is not capturing the surrounding brain parenchyma or CSF.
To begin editing, click on the segment to work on, and the segment name will become highlighted.
Click on the Paint or Draw tool in the Effects section of Segment Editor to begin manual segmentation.
NOTE: It is best to start segmenting in one plane (coronal, axial, or sagittal), and after segmentation has been completed in all of the slices, move to other planes to check and refine the manual segmentation. It is suggested that the user starts with the axial or coronal planes as the lateral ventricle choroid plexus is more easily seen in these views.
When using the Draw tool, left-click and hold down to draw a contour at the boundary of the lateral ventricle choroid plexus. Once traced, right-click to fill in the drawn-in area.
When using the Paint tool, first select the diameter of the brush to be used for painting. A 3% or 5% brush is suggested for a more precise delineation of the choroid plexus, while 10% can be useful for larger selections.
For either tool, utilize Paint or Erase to rectify any erroneous delineations by adding or removing selections.
NOTE: Referencing other planes of view can aid in identifying the lateral ventricle choroid plexus structure versus other brain structures, such as surrounding gray matter, the fornix, corpus callosum, or hippocampus. The user is encouraged to exclude brain scans that have choroid plexus cysts identified.
Use the level of the red nucleus as a landmark for stopping the segmentation of the choroid plexus in the lateral ventricles.

7. Delineating third and fourth ventricle choroid plexus ROIs

NOTE: Higher resolution T1w images (such as 0.7 or 0.8 mm) and those obtained on a 7T MRI would provide a more accurate and reliable manual segmentation of the third and fourth ventricle choroid plexus. Segmenting the third and fourth ventricle choroid plexus is more difficult than the lateral ventricle choroid plexus as these regions can be much smaller and with fewer voxels to delineate.

For the third ventricle choroid plexus, begin in the sagittal plane and use the foramen of Monro, fornix, corpus callosum, thalamus, and internal cerebral vein as reference points to pinpoint the choroid plexus in the 3^rd ventricle. Moving between slices within the same plane can aid in determining whether a region is the fornix, thalamus, vein, or third ventricle choroid plexus.
1. Once edits have been made in the sagittal plane, navigate to the remaining views (axial and coronal) to ensure that the manual segmentation of the third ventricle choroid plexus is not selecting the surrounding brain parenchyma or CSF.
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. Moving between slices within the same plane can aid in determining whether a region is the cerebellum, cerebellar tonsil, inferior medullary velum or 4^th ventricle choroid plexus.
1. Once edits have been completed in the sagittal plane, move to the remaining views (axial and coronal) to ensure that the manual segmentation of the fourth ventricle choroid plexus is not selecting surrounding brain parenchyma or CSF.

8. Calculating the volumes of the choroid plexus

From the Modules drop-down menu, navigate to Quantification and select Segment Statistics.
Under Inputs, select the new segmentation map for quantifying under the Segmentation tool and choose the MRI volume from the Scalar volume. For Output table (under Output), choose the Table option. Once complete, press Apply, and a table containing the volume of the choroid plexus will appear in various units.

9. Saving the segments and volume results

Click the Save button in the upper left corner of the toolbar to save generated files.
Save the segmentation files as .nrrd (3D slicer file), .nii.gz (NIFTI file) or .tsv (table file).

10. Determining accuracy, performance, and agreement of the segmentation

NOTE: It is recommended to use the MONAI package (see Table of Materials), which describes the Dice Coefficient (DC) and the DeepMind average Surface Distance (avgSD). Details on DC and avgSD are described below. In order to compute these metrics, readers will need to know how to program (e.g., python, read images from disk, re-format the data to the appropriate input arrays for these functions). There is no user-friendly package that includes all these metrics.

The DC score is a standard approach to quantify the overlap of two geometric domains. To compute the average DC score between two segmentations, provide two tensors y_pred and y, i.e., multi-frame images with one frame for each binarized label image. Tensors y_pred and y may contain segmentations of two different manual raters, repeated segmentations of the same rater, or automated prediction and manual ground truth.
1. Use the function monai.metrics.compute_meandice to calculate the mean DC score.
2. Generate suitable binary label tensors with monai.transforms.post.
  NOTE: The include_background parameter can be set to False to exclude the first category (channel index 0) from the DC computation, which is, by convention, assumed to be a background.
Consider the avgSD score less common, and note that the approach can differ as multiple definitions exist for surface distance. For example, use the max distance (also known as Hausdorff distance, highly sensitive to outliers), the mean distance (as described here), and the 95th percentile (highly robust) as frequently used measures.
1. Use the function compute_average_surface_distance to calculate the avgSD score.
2. Ensure that this function computes the Average Surface Distance from y_pred 세스 y under the default setting.
3. In addition, if symmetric = True, ensure that the average symmetric surface distance between these two inputs is returned.
Perform the statistical analysis of the DC and avgSD score across multiple cases can be performed by using the robust Wilcoxon signed-rank test for paired analysis.
Consider using the Intraclass correlation coefficient (ICC) as another commonly used method to determine whether multiple participants can be rated reliably by different raters. Remember that ICC operates on a set of paired measurements (e.g., the volume) of segmentations and not on the segmentation images directly. To compute ICC, use R software and R Studio (see Table of Materials), which makes the process straightforward.
1. Download the package using install.packages("psych") and load the library(psych).
2. Enter the data frame, which includes the participants (rows) and a rater in each column, by using Data <- data.frame(df). Then visualize the measurements using plot (Data).
3. To run ICC, use ICC(Data), which generates a table of the different types of ICC, e.g., to obtain the inter- or intra-rater scores.

Representative Results

The proposed method has undergone iterative refinement for the lateral ventricle choroid plexus, involving extensive testing on a cohort of 169 healthy controls and 340 patients with clinically high risk for psychosis³⁰. Using the technique described above, the authors obtained high intra-rater accuracy and reliability with a DC = 0.89, avgHD = 3.27 mm³, and single-rater ICC = 0.9730, demonstrating the strength of the protocol described herein.

Handling quality control issues and 3D Slicer settings
Before starting the segmentation process, it is necessary to check the quality of the brain scan to ensure that there is no head motion or artifacts that interfere with manual segmentation (Figure 1A). Next, brightness and contrast may be adjusted to assist with better visualization of the choroid plexus. Some brain scans may have head motion, and it is important to determine whether the artifact would adversely impact the delineation of the choroid plexus (Figure 1B). Additionally, images with brightness and contrast artifacts make it difficult to distinguish the borders of the choroid plexus (Figure 1C,D). In this case, try adjusting the brightness and contrast until it is suitable for manual segmentation. Ensure that brain scans that cannot be easily segmented for the choroid plexus are excluded.

Lateral ventricle choroid plexus segmentation
As shown in Figure 2, five main parts are used to load and display the images (part 1), select different 3D slicer functions (part 2), tools for segmenting the lateral choroid plexus (part 3), visualizing the axial, coronal, and sagittal images (part 4), calculating the volume of the lateral ventricle choroid plexus (part 5), and saving the results from the manual segmentation. The T1w brain scan can be uploaded using the Welcome to Slicer interface by downloading sample data from MRHead dataset in 3D Slicer (Figure 3) or importing the NIFTI or DICOM file from an existing dataset (Figure 4A,B). There is also an option in this panel to edit the brightness and contrast of the image (Figure 4C). After loading the T1w brain scan, it will be displayed in the slice view interface and prepared for lateral ventricle choroid plexus segmentation. Manual segmentation is created using the Segment Editor module (Figure 5A), and the master volume name can be confirmed in Figure 5B. In Figure 5C, the labels for the right and left lateral ventricle choroid plexus can be added and labeled in different colors (Figure 5C), and the region of interest itself can be delineated by using the Draw or Paint Tool (Figure 5D). Figure 6 labels the lateral ventricle choroid plexus and its surrounding brain structures, such as the caudate nucleus, hippocampus, fornix, and the third ventricle, which provides landmarks for the segmentation of the lateral ventricle choroid plexus in some of the more complex regions. To generate and extract choroid plexus volume data from the manual segmentations, select the Segment Statistics module (Figure 7A). There are a few options to select from for outputting the data (Figure 7B). The new files containing the calculated lateral ventricle choroid plexus volume can now be saved by pressing the Save button (Figure 7C).

Third and fourth ventricle choroid plexus segmentation
As seen in Figure 8, the 3^rd ventricle choroid plexus can be easily viewed in the lower left panel depicting the sagittal plane. Notably, the Foramen of Monro can be observed arching below the corpus callosum, with the choroid plexus highlighted within the third ventricle in green. The third ventricle and the third ventricle choroid plexus can also be viewed in the axial and coronal planes (upper left and lower right panels of Figure 8, respectively). Finally, a 3D rendering of the third ventricle choroid plexus is shown in the upper right panel of Figure 8. Figure 9 labels the third ventricle choroid plexus and its surrounding brain structures, including the corpus callosum, fornix, thalamus, internal cerebral vein, and third ventricle, which provides landmarks for the segmentation of the third ventricle choroid plexus in some of the more complex regions.

The fourth ventricle choroid plexus is harder to view and can be seen in Figure 10. The sagittal and coronal planes (lower left and lower right panels of Figure 10) allow for the best viewing of its structure. Care must be taken to ensure that parts of the cerebellum or the fourth ventricle itself are not delineated as choroid plexus. Figure 11 labels the fourth ventricle choroid plexus and its surrounding brain structures, including the medulla, pons, superior cerebella peduncle, inferior medullary velum, and fourth ventricle, which provides landmarks for the segmentation of the 4^th ventricle choroid plexus in some of the more complex regions.

Segmentation accuracy, similarity, and agreement
Segmentation of neuroanatomical structures can be directly compared in an image viewer, but the similarity is sometimes difficult to be assessed visually. Therefore, quantitative measures such as the DC⁵², measuring percent overlap, and the avgSD⁵³, measuring distances between the boundary surfaces of the delineated structures, are used to compare predictions with ground truth or manual segmentations across or within raters to assess reliability. As depicted in Figure 12A, the DC for two 3D segmentations G and P is simply the volume of the overlap (intersection) divided by the average volume⁵³:

where | . | represents volume. It measures overlap on a scale between 0 and 1, where a value of 1 indicates exact agreement and 0 disjoint segmentations and is often multiplied by 100 to represent a percent overlap. The average surface distance (ASD) measures the average distance (in mm) between all points x on the boundary of G ( bd(G) ) to the boundary of P and vice-versa (Figure 12B). It is defined as

with distance representing the minimum of the Euclidean norm⁵³. In contrast to the DC, a smaller ASD indicates better capture of the segmentation boundaries, with a value of zero being the minimum (perfect match). Note that sometimes also, the maximum distance or the 95^th percentile is used instead of the average, where the maximum is highly sensitive to single outliers, while the 95^th percentile is robust but may miss small but relevant segmentation errors.

The agreement of volume estimates (not of the segmentations directly) between a set of paired segmentations can be measured using ICC⁵⁴. This can be accomplished by having multiple participants rated by multiple raters (interclass ICC) or by the same rater (intraclass ICC) (Figure 12C). ICC scores range from 0 (poor reliability) to 1 (excellent reliability). For inter-rater reliability, it is suggested to use ICC1 (one-way fixed-effects model) for datasets where each segmentation is done by a different rater selected at random. Additionally, for datasets where multiple raters, chosen at random, work on the same segmentation, it is recommended to use ICC2 (two-way random-effects model) to test for absolute agreement in the segmentations. Finally, for intra-rater reliability, it is recommended to use ICC3 (two-way mixed effects model) (Figure 12C).

Figure 1: Brain scan quality control. (A) Brain scan with good contrast and brightness, no evidence of artifacts, and no head motion. (B) Brain scan showing head motion (red arrow). (C) Brain scan with high brightness and low contrast or (D) low brightness and high contrast. Please click here to view a larger version of this figure.

Figure 2: The segmentation of the lateral ventricle choroid plexus in 3D Slicer. (1) is used to load the DICOM or NIFTI images and to save the results. (2) consists of a drop-down menu that can be used to enter the Segment Editor module (yellow arrow), which is used to segment the choroid plexus. The Quantification module (blue arrow) can also be selected here to calculate the volume of the choroid plexus. (3) shows the segment toolbar, which includes the draw, paint, and erase tools. (4) demonstrates the choroid plexus in axial, sagittal, and coronal views of the T1w image. The 3D rendering of the choroid plexus is also shown in the upper right corner. (5) displays the volume results from the manual choroid plexus segmentation, calculated using the Segment Statistics module. The final results can be saved using the save button mentioned in (1). Please click here to view a larger version of this figure.

Figure 3: Loading 3D Slicer sample data. This figure demonstrates how to download the sample data from the 3DSlicer interface. First, "Download Sample Data" must be selected, and then "MRHead" must be chosen, which displays the axial, sagittal, and coronal views of the brain scan on the right side of the screen. Please click here to view a larger version of this figure.

Figure 4: Loading the T1w brain scan. This figure demonstrates how to upload the T1w brain scan using either NIFTI (left panel) or DICOM (right panel) files. (A) For NIFTI files, either the "Choose Directory to Add" or "Choose File(s) to Add" must be selected, followed by selecting "OK". (B) For DICOM files, selecting "Add DICOM Data", followed by "Import DICOM files" and then pressing "OK" is needed. These two approaches will display the axial, sagittal, and coronal views of the brain scan on the right side of the screen. (C) To adjust the brightness and contrast of the images, the red button must be selected. Please click here to view a larger version of this figure.

Figure 5: Lateral ventricle choroid plexus segmentation. After the T1w brain scan has been loaded into the 3D Slicer. (A) Selecting the "Segmentation Editor" module. (B) Confirming the module and the master volume for manual segmentation of the lateral ventricle choroid plexus. (C) Creating labels for the right and left lateral ventricle choroid plexus. (D) Using the "draw" and "paint" tools to manually delineate the lateral ventricle choroid plexus. Please click here to view a larger version of this figure.

Figure 6: Adjacent structures to the lateral ventricle choroid plexus. Adjacent brain structures include the fornix, caudate nucleus, hippocampus, and the third ventricle. Please click here to view a larger version of this figure.

Figure 7: Volume calculation. Calculating the volume of the choroid plexus and saving the segments and volume results. (A) Selecting the Segment Statistics module. (B) Selecting for outputting the data. (C) Pressing the Save button to save the new files containing the calculated lateral ventricle choroid plexus volume. Please click here to view a larger version of this figure.

Figure 8: Third ventricle choroid plexus segmentation. Depicted here are the axial, coronal, and sagittal views of the third ventricle choroid plexus that has been manually segmented using the 3D Slicer. The top right corner shows a 3D rendering of the third ventricle choroid plexus. Please click here to view a larger version of this figure.

Figure 9: Adjacent structures to the third ventricle choroid plexus. Adjacent brain structures include the fornix, internal cerebral vein, thalamus, corpus callosum, and 3^rd ventricle. Please click here to view a larger version of this figure.

Figure 10: Fourth ventricle choroid plexus segmentation. Depicted here are the axial, coronal, and sagittal views of the fourth ventricle choroid plexus that has been manually segmented using the 3D Slicer. The top right corner shows a 3D rendering of the fourth ventricle choroid plexus. Please click here to view a larger version of this figure.

Figure 11: Adjacent structures to the fourth ventricle choroid plexus. Adjacent brain structures include the medulla oblongata, pons, cerebellum, cerebellar vermis, and cerebellar tonsils. Please click here to view a larger version of this figure.

Figure 12: Determining segmentation accuracy, performance, and agreement. (A) Depicting how the percent overlap is calculated using the Dice Coefficient (DC) score. (B) The average surface distance (avgSD) measures the distances between the boundary surfaces of the delineated structures in order to compare predictions with ground truth, or manual segmentations across or within raters to assess reliability. (C) The Intraclass Correlation Coefficient (ICC) can be used for inter-rater (repeated measurements of the same subject) or intra-rater (multiple measurements from the same raters) reliability analysis. A representative example and output are provided. Please click here to view a larger version of this figure.

Discussion

Critical steps of the protocol
Three critical steps require special attention when implementing this protocol. First, checking the quality and contrast of MR images is key to ensuring accurate segmentation. If the quality of the image is too poor, or the contrast is too low or too high, it may lead to the inaccurate delineation of the choroid plexus. The contrast for the image can be adjusted by viewing the image's grayscale value or by calibrating the values to enhance the contrast between the gray matter nuclei and gray matter. Second, the raters need to be familiar with the anatomy of the choroid plexus and have specialized training. If raters are unfamiliar with the anatomy of the choroid plexus and adjacent brain regions, they may segment the choroid plexus incorrectly, making the choroid plexus' volume inaccurate. Lastly, it is important to evaluate the intra- and inter-rater reproducibility to ensure that raters carrying out manual segmentation can reproduce their own, as well as other raters' segmentation of the choroid plexus. These numbers are also highly relevant when validating automated segmentation tools at a later stage. Additionally, if the dataset is fixed, and if multiple raters will be used for manual segmentation, then it is recommended that the same window setting be used so that the raters are looking at the same image with the same contrast and brightness. If the window setting changes between raters looking at the same image, then the same image might be segmented differently.

Modifications and troubleshooting
Users can make some modifications to this protocol. First, choroid plexus tissue located in the para septal area and the inferno-anterior-lateral part of the temporal horn, which is adjacent to the septum pellucidum, fornix, and hippocampus, can make the segmentation of the choroid plexus challenging. To address this difficulty, it would be suggested to conduct the segmentation of the choroid plexus in all three dimensions, and a reference (Figure 6) is provided for segmenting the choroid plexus in these complex regions. Second, it is also important to know when to stop the segmentation. For the choroid plexus in the lateral and third ventricles, the red nucleus can be used as a stopping landmark, while for the fourth ventricle choroid plexus, the foramen of Magendie can be used as a stopping point. Third, challenges may exist when distinguishing the boundary between the choroid plexus and CSF in the posterior-basal part of the lateral ventricles. To address this concern, signal intensity and anatomical considerations can be used to aid the rater in making appropriate segmentation decisions. Fourth, if a low-resolution image is being used, it would be recommended to be more conservative in the segmentation procedure and to prioritize using contrast-enhanced imaging to validate the segmentation of the choroid plexus in this temporal region. If contrast-enhanced imaging is not available, then it would be suggested to exclude this region from the segmentation process. However, if a high-resolution image is being used, then it would be recommended to be more liberal in the segmentation procedure. Also, if the demarcation between the choroid plexus and the brain parenchyma can be made on a high-resolution image of the temporal horn, then a contrast-enhanced image would not be necessary. Fifth, 3D Slicer can run on a touchscreen computer where a stylus pen instead of a mouse can be used to enhance the tracing of the choroid plexus. However, this software is not currently available on the iPad. Lastly, software crashing issues may be encountered on some computers when the choroid plexus of more than ten subjects have been delineated in succession. In this case, clicking the Save button frequently can prevent data loss caused by the software crash.

Limitations
While manual segmentation of the choroid plexus is the gold standard for obtaining accurate volume data, there are several limitations related to the type and quality of the scan, as well as the experience of the rater²¹. For example, choroid plexus size can vary depending on age or disease state, which may impact the size of the ventricle and the choroid plexus. Thus, the choroid plexus may appear small in young, healthy individuals, making it difficult to manually segment. This issue may be compounded if the image is of poorer resolution (1.2 or 1.5 mm isovoxel) and/or captured using a 1.5 T MRI scanner. Manual segmentation of the choroid plexus could additionally be impacted by the brightness and contrast of the image, making it difficult to identify the boundaries, resulting in over or underestimation of the volume. Additionally, the third and fourth ventricle choroid plexus are small structures, which can be challenging to segment properly if a higher resolution image is not available (0.7 or 0.8 mm isovoxel). A limitation to using a 3D Slicer instead of other open-source manual segmentation software is the inability to perform image segmentation simultaneously in three dimensions, a feature offered through ITK-SNAP⁵¹ that can improve the speed of image segmentation of the choroid plexus. Additionally, manual segmentation is a time-consuming and tedious task, making the study of the choroid plexus in large cohorts with thousands or tens of thousands of individuals impractical, highlighting the need for accurate automatic choroid plexus segmentation tools. Lastly, simply counting choroid plexus voxels without accounting for the partial volume effects of CSF or white matter may introduce errors in the volume measurement.

Significance with respect to existing methods
The dependence on FreeSurfer for choroid plexus segmentation, which has poor accuracy and doesn't segment the third and fourth ventricle choroid plexus, limits the foundational work that can be completed to better understand the role of the choroid plexus in health and disease. Additionally, a more accurate delineation of the choroid plexus may also be leveraged by the Alzheimer's neuroimaging community to reduce the contamination of medial temporal tau PET signal by off-target binding in the choroid plexus⁵⁵. While initial adaptations of machine learning (GMM) and deep-learning techniques (3D U-Net, nnU-Net, Axial-MLP 8) to choroid plexus labels have improved the segmentation accuracy upon FreeSurfer-derived choroid plexus labels⁴⁶^,⁴⁸^,⁴⁹, methods are unfortunately only trained and evaluated in small, homogeneous datasets, neither publicly available nor easy-to-use tools and incomplete only including the choroid plexus within the lateral ventricles. One caveat is that at the time of the resubmission of this protocol, there was an article published by Yazdan-Panah et al. where they conducted manual segmentation of the lateral ventricle choroid plexus using ITK-SNAP⁵⁶. They used these manually segmented images to train a 2-step 3D U-Net and demonstrated an average DC of 0.72 with the ground truth, and it outperformed FreeSurfer and FastSurfer-based segmentations⁵⁶. The generalizability to other resolutions, scanners, ages, and multiple diseases has not been established and is, in fact, unlikely given the challenge of the domain transfer.

Future applications
Because of the limitations noted above, a protocol for accurately segmenting the choroid plexus is needed. Furthermore, in order to create an automated segmentation tool for the choroid plexus, which can be challenging to develop due to the nature of this structure, a comprehensive annotated dataset of the choroid plexus is needed spanning various parameters and combining it with a set of methodological innovations for the state-of-the-art open-source software, FastSurfer⁴²^,⁴³, an advanced and scalable deep learning-based neuroimaging pipeline for automated cortical and subcortical segmentation. FastSurferCNN has been shown to outperform 3D U-Net, SDNet, and QuickNAT models for cortical and subcortical segmentation of close to 100 structures with an average DCs > 85⁴². Thus, a large and comprehensive annotation of the choroid plexus can be used with FastSurfer to significantly extend to (1) a 3D architecture with improved internal augmentation techniques, (2) the capability to also predict – for the first time – partial volume estimates directly, as well as (3) output segmentations at higher resolutions (super-resolution) for data harmonization. The authors plan to further work on adapting and developing FastSurfer to create a highly accurate choroid plexus segmentation tool for the lateral, third, and fourth ventricles and share the same openly with the research community.

Disclosures

The authors have nothing to disclose.

Acknowledgements

This work was supported by a National Institute of Mental Health Award R01 MH131586 (to P.L and M.R), R01 MH078113 (to M.K), and a Sydney R Baer Jr Foundation Grant (to P.L).

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.

References

Lun, M. P., Monuki, E. S., Lehtinen, M. K. Development and functions of the choroid plexus-cerebrospinal fluid system. Nature Reviews Neuroscience. 16 (8), 445-457 (2015).
Dani, N., Herbst, R. H., McCabe, C. A cellular and spatial map of the choroid plexus across brain ventricles and ages. Cell. 184 (11), 3056-3074 (2021).
Kaiser, K., Bryj, a. V. Choroid plexus: the orchestrator of long-range signalling within the CNS. IJMS. 21 (13), 4760 (2020).
Damkier, H. H., Brown, P. D., Praetorius, J. Cerebrospinal fluid secretion by the choroid plexus. Physiological Reviews. 93, 46 (2013).
Liddelow, S. A. Development of the choroid plexus and blood-CSF barrier. Frontiers in Neuroscience. 9, 00032 (2015).
Gato, A., Alonso, M. I., Lamus, F., Miyan, J. Neurogenesis: A process ontogenically linked to brain cavities and their content, CSF. Seminars in Cell & Developmental Biology. 102, 21-27 (2020).
Spatazza, J., Lee, H. H. C., Di Nardo, A. A. Choroid-plexus-derived Otx2 homeoprotein constrains adult cortical plasticity. Cell Reports. 3 (6), 1815-1823 (2013).
Kim, S., Hwang, Y., Lee, D., Webster, M. J. Transcriptome sequencing of the choroid plexus in schizophrenia. Translational Psychiatry. 6 (11), e964-964 (2016).
Myung, J., Schmal, C., Hong, S. The choroid plexus is an important circadian clock component. Nature Communications. 9 (1), 1062 (2018).
Quintela, T., Furtado, A., Duarte, A. C., Gonçalves, I., Myung, J., Santos, C. R. A. The role of circadian rhythm in choroid plexus functions. Progress in Neurobiology. 205, 102129 (2021).
Gorlé, N., Blaecher, C., Bauwens, E., et al. The choroid plexus epithelium as a novel player in the stomach-brain axis during Helicobacter infection. Brain, Behavior, and Immunity. 69, 35-47 (2018).
Zappaterra, M. W., Lehtinen, M. K. The cerebrospinal fluid: regulator of neurogenesis, behavior, and beyond. Cellular and Molecular Life Sciences. 69 (17), 2863-2878 (2012).
Cardia, E., Molina, D., Abbate, F. Morphological modifications of the choroid plexus in a rodent model of acute ventriculitis induced by gram-negative liquoral sepsis: Possible implications in the pathophysiology of hypersecretory hydrocephalus. Child’s Nervous System. 11 (9), 511-516 (1995).
Coisne, C., Engelhardt, B. Tight junctions in brain barriers during central nervous system inflammation. Antioxidants & Redox Signaling. 15 (5), 1285-1303 (2011).
Szmydynger-Chodobska, J., Strazielle, N., Gandy, J. R. Posttraumatic Invasion of monocytes across the blood-cerebrospinal fluid barrier. Journal of Cerebral Blood Flow & Metabolism. 32 (1), 93-104 (2012).
Pellegrini, L., Albecka, A., Mallery, D. L. SARS-CoV-2 infects the brain choroid plexus and disrupts the blood-csf barrier in human brain organoids. Cell Stem Cell. 27 (6), 951-961 (2020).
Bitanihirwe, B., Lizano, P., Woo, T. Deconstructing the functional neuroanatomy of the choroid plexus: an ontogenetic perspective for studying neurodevelopmental and neuropsychiatric disorders. Review at Molecular Psychiatry. , (2022).
Ramaekers, V., Sequeira, J. M., Quadros, E. V. Clinical recognition and aspects of the cerebral folate deficiency syndromes. Clinical Chemistry and Laboratory Medicine. 51 (3), 0543 (2012).
Lizano, P., Lutz, O., Ling, G. Association of choroid plexus enlargement with cognitive, inflammatory, and structural phenotypes across the psychosis spectrum. AJP. 176 (7), 564-572 (2019).
Kim, H., Lim, Y. M., Kim, G. Choroid plexus changes on magnetic resonance imaging in multiple sclerosis and neuromyelitis optica spectrum disorder. Journal of the Neurological Sciences. 415, 116904 (2020).
Bannai, D., Lutz, O., Lizano, P. Neuroimaging considerations when investigating choroid plexus morphology in idiopathic psychosis. Schizophrenia Research. 224, 19-21 (2020).
Zhou, G., Hotta, J., Lehtinen, M. K., Forss, N., Hari, R. Enlargement of choroid plexus in complex regional pain syndrome. Scientific Reports. 5 (1), 14329 (2015).
Jacob, F., Pather, S. R., Huang, W. K. Human pluripotent stem cell-derived neural cells and brain organoids reveal SARS-CoV-2 neurotropism predominates in choroid plexus epithelium. Cell Stem Cell. 27 (6), 937-950 (2020).
Yang, A. C., Kern, F., Losada, P. M. Dysregulation of brain and choroid plexus cell types in severe COVID-19. Nature. 595 (7868), 565-571 (2021).
Lin, Y. J., Chiu, N. C., Chen, H. J., Huang, J. Y., Ho, C. S. Cranial ultrasonographic screening findings among healthy neonates and their association with neurodevelopmental outcomes. Pediatrics & Neonatology. 62 (2), 158-164 (2021).
Chang, H., Tsai, C. M., Hou, C. Y., Tseng, S. H., Lee, J. C., Tsai, M. L. Multiple subependymal pseudocysts in neonates play a role in later attention deficit hyperactivity and autistic spectrum disorder. Journal of the Formosan Medical Association. 118 (3), 692-699 (2019).
Levman, J., Vasung, L., MacDonald, P. Regional volumetric abnormalities in pediatric autism revealed by structural magnetic resonance imaging. International Journal of Developmental Neuroscience. 71 (1), 34-45 (2018).
Taft, A. E. A note on the pathology of the choroid plexus in general paralysis. Archives of Neurology & Psychiatry. 7 (2), 177 (1922).
D, S. R. The choroid plexus in organic diseases of the brain and of schizophreina. The Journal of Nervous and Mental Disease. 56, 21-26 (1921).
Bannai, D., Reuter, M., Hegde, R. Linking choroid plexus enlargement with plasma analyte and structural phenotypes in clinical high risk for psychosis: a multisite neuroimaging study. BioRxiv. , (2022).
Egorova, N., Gottlieb, E., Khlif, M. S., Spratt, N. J., Brodtmann, A. Choroid plexus volume after stroke. International Journal of Stroke. 14 (9), 923-930 (2019).
Ricigliano, V. A., Morena, E., Colombi, A. Choroid plexus enlargement in inflammatory multiple sclerosis: 3.0-T MRI and translocator protein PET evaluation. Radiology. 301 (1), 166-177 (2021).
Tadayon, E., Pascual-Leone, A., Press, D., Santarnecchi, E. Choroid plexus volume is associated with levels of CSF proteins: relevance for Alzheimer’s and Parkinson’s disease. Neurobiology of Aging. 89, 108-117 (2020).
Choi, J. D., Moon, Y., Kim, H. J., Yim, Y., Lee, S., Moon, W. J. Choroid plexus volume and permeability at brain MRI within the Alzheimer Disease clinical spectrum. Radiology. 304 (3), 635-645 (2022).
Althubaity, N., Schubert, J., Martins, D. Choroid plexus enlargement is associated with neuroinflammation and reduction of blood-brain barrier permeability in depression. NeuroImage: Clinical. 33, 102926 (2022).
Senay, O., et al. Choroid plexus volume in individuals with early course and chronic psychosis – a magnetic resonance imaging study. Schizophrenia Bulletin. , (2022).
Fischi, B. FreeSurfer. NeuroImage. 62 (2), 774-781 (2012).
Fischl, B., et al. Cortical folding patterns and predicting cytoarchitecture. Cerebral Cortex. 18 (8), 1973-1980 (2008).
Fischl, B., vander Kouwe, A., Destrieux, C. Automatically parcellating the human cerebral cortex. Cerebral Cortex. 14 (1), 11-22 (2004).
Patenaude, B., Smith, S. M., Kennedy, D. N., Jenkinson, M. A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage. 56 (3), 907-922 (2011).
Huo, Y., Xu, Z., Xiong, Y. 3D whole brain segmentation using spatially localized atlas network tiles. NeuroImage. 194, 105-119 (2019).
Henschel, L., Conjeti, S., Estrada, S., Diers, K., Fischl, B., Reuter, M. FastSurfer – A fast and accurate deep learning based neuroimaging pipeline. NeuroImage. 219, 117012 (2020).
Henschel, L., Kügler, D., Reuter, M. FastSurferVINN: Building resolution-independence into deep learning segmentation methods-A solution for HighRes brain MRI. NeuroImage. 251, 118933 (2022).
Jovicich, J., Czanner, S., Han, X. MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. NeuroImage. 46 (1), 177-192 (2009).
Zaretskaya, N., Fischl, B., Reuter, M., Renvall, V., Polimeni, J. R. Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE. NeuroImage. 165, 11-26 (2018).
Tadayon, E., Moret, B., Sprugnoli, G., Monti, L., Pascual-Leone, A., Santarnecchi, E. Improving choroid plexus segmentation in the healthy and diseased brain: Relevance for Tau-PET imaging in dementia. Journal of Alzheimer’s Disease. 74 (4), 1057-1068 (2020).
Schmidt-Mengin, M., Ricigliano, V. A. G., Bodini, B., Išgum, I., Colliot, O. Axial multi-layer perceptron architecture for automatic segmentation of choroid plexus in multiple sclerosis. Medical Imaging 2022: Image Processing. SPIE. , (2022).
Zhao, L., Feng, X., Meyer, C. H., Alsop, D. C. Choroid plexus segmentation using optimized 3D U-Net. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE. , 381-384 (2020).
Schmidt-Mengin, M., et al. Axial multi-layer perceptron architecture for automatic segmentation of choroid plexus in multiple sclerosis. arXiv. , (2021).
Egger, J., Kapur, T., Nimsky, C., Kikinis, R., Muñoz-Barrutia, A. Pituitary adenoma volumetry with 3D Slicer. PLoS ONE. 7 (12), 51788 (2012).
Yushkevich, P. A., Piven, J., Hazlett, H. C., et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage. 31 (3), 1116-1128 (2006).
Dice, L. R. Measures of the amount of ecologic association between species. Ecology. 26 (3), 297-302 (1945).
Aydin, O. U., Taha, A. A., Hilbert, A. On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking. European Radiology Experimental. 5 (1), (2021).
Shrout, P. E., Fleiss, J. L. Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin. 86 (2), 420-428 (1979).
Pawlik, D., Leuzy, A., Strandberg, O., Smith, R. Compensating for choroid plexus based off-target signal in the hippocampus using 18F-flortaucipir PET. NeuroImage. 221, 117193 (2020).
Yazdan-Panah, A., Schmidt-Mengin, M., Ricigliano, V. A. G., Soulier, T., Stankoff, B., Colliot, O. Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis. NeuroImage: Clinical. 38, 103368 (2023).

Play Video

PDF

DOI

DOWNLOAD MATERIALS LIST

Cite This Article

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).