segmentation of the choroid plexus using manual delineations

Enhancing Neuroimaging Analysis in Brain Disorders

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.

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies 

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

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  

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.

segmentation-of-the-choroid-plexus-using-manual-delineations

Research

JoVE Journal

Neuroscience

277 ビュー.  Beth Israel Deaconess Medical Center. まず、ツールバーの左上隅にあるDCMボタンをクリックし、DICOMファイルのインポートボタンをクリックして、医療またはDICOMデータのデジタルイメージングと通信をインポートします。画像の明るさを調整するには、[ウィンドウと音量レベルの調整]をクリックするか、画像を左クリックしてマウスを上下にドラッグします。次に、画像スライスを左クリックし、マウス?...