Name: लेखक स्पॉटलाइट: एनाटॉमी में अंतराल को पाटना और एल्गोरिथम अध्ययन के लिए एक फाउंडेशन स्थापित करना
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

इस काम को नेशनल इंस्टीट्यूट ऑफ मेंटल हेल्थ अवार्ड R01 MH131586 (पीएल और एमआर को), R01 MH078113 (एमके को), और सिडनी आर बेयर जूनियर फाउंडेशन ग्रांट (पीएल को) द्वारा समर्थित किया गया था।

वर्तमान प्रोटोकॉल बेथ इसराइल डेकोनेस मेडिकल सेंटर में संस्थागत समीक्षा बोर्ड द्वारा अनुमोदित किया गया था. एक मस्तिष्क एमआरआई स्कैन के साथ एक स्वस्थ विषय जो कलाकृतियों या आंदोलन से मुक्त था, इस प्रोट?...

मस्तिष्क विकारों में न्यूरोइमेजिंग विश्लेषण को बढ़ाना

प्रोटोकॉल के महत्वपूर्ण कदम तीन महत्वपूर्ण कदम जब इस प्रोटोकॉल को लागू करने के लिए विशेष ध्यान देने की आवश्यकता है. सबसे पहले, एमआर छवियों की गुणवत्ता और कंट्रास्ट की जांच करना सटीक विभाजन सुन?...

<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

कोरॉइड प्लेक्सस को न्यूरोडेवलपमेंट और मस्तिष्क विकारों की एक श्रृंखला में फंसाया गया है। साक्ष्य दर्शाता है कि रंजित जाल मस्तिष्क परिपक्वता, प्रतिरक्षा / भड़काऊ विनियमन, और व्यवहार / संज्ञानात्मक कार्य के लिए महत्वपूर्ण है। हालांकि, वर्तमान स्वचालित न्यूरोइमेजिंग विभाजन उपकरण पार्श्व वेंट्रिकल रंजित जाल को सही और मज़बूती से विभाजित करने में खराब हैं। इसके अलावा, कोई मौजूदा उपकरण नहीं है जो मस्तिष्क के तीसरे और चौथे निलय में स्थित कोरॉइड प्लेक्सस को खंडित करता है। इस प्रकार, पार्श्व, तीसरे, और चौथे वेंट्रिकल में रंजित जाल खंड करने के लिए कैसे एक प्रोटोकॉल neurodevelopmental और मस्तिष्क विकारों में रंजित जाल की जांच अध्ययन की विश्वसनीयता और प्रतिकृति बढ़ाने के लिए आवश्यक है. यह प्रोटोकॉल DICOM या NIFTI छवियों के आधार पर रंजित जाल के लिए 3D स्लाइसर में अलग से लेबल की गई फ़ाइलें बनाने के लिए विस्तृत चरण प्रदान करता है। रंजित जाल मैन्युअल अक्षीय, धनु और T1w छवियों के कोरोनल विमानों का उपयोग कर खंडित किया जाएगा, निलय की सीमा ग्रे या सफेद पदार्थ संरचनाओं से voxels को बाहर करने के लिए सुनिश्चित करने. रंजित जाल और इसकी शारीरिक सीमाओं के स्थानीयकरण में सहायता के लिए विंडोिंग को समायोजित किया जाएगा। सटीकता और विश्वसनीयता का आकलन करने के तरीके इस प्रोटोकॉल के हिस्से के रूप में प्रदर्शित किया जाएगा. मैनुअल परिसीमन का उपयोग कर रंजित जाल के सोने के मानक विभाजन खुले तौर पर जीवन भर में और विभिन्न मस्तिष्क विकारों के भीतर रंकश जाल में परिवर्तन स्पष्ट करने के लिए साझा किया जा सकता है कि बेहतर और अधिक विश्वसनीय स्वचालित विभाजन उपकरण विकसित करने के लिए इस्तेमाल किया जा सकता है.

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.

लेखक स्पॉटलाइट: एनाटॉमी में अंतराल को पाटना और एल्गोरिथम अध्ययन के लिए एक फाउंडेशन स्थापित करना 

मस्तिष्क एमआरआई का उपयोग कर मानव रंजित जाल के मैनुअल विभाजन

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.

प्रस्तावित विधि पार्श्व वेंट्रिकल रंजित जाल के लिए पुनरावृत्त शोधन आया है, जिसमें 169 स्वस्थ नियंत्रणों के एक समूह पर व्यापक परीक्षण और मनोविकृति30 के लिए नैदानिक रूप से उच्च जोखिम वाले 340 रोगिय?...

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

मस्तिष्क में रंजित जाल की महत्वपूर्ण भूमिका के बावजूद, विश्वसनीय स्वचालित विभाजन उपकरणों की कमी के कारण इस संरचना के न्यूरोइमेजिंग अध्ययन दुर्लभ हैं। वर्तमान प्रोटोकॉल का उद्देश्य रंजित जाल के स्वर्ण-मानक मैनुअल विभाजन को सुनिश्चित करना है जो भविष्य के न्यूरोइमेजिंग अध्ययनों को सूचित कर सकता है।

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

हम मैन्युअल मस्तिष्क के पार्श्व तीसरे और चौथे निलय के भीतर रंजित जाल खंडित करने के लिए एक सुलभ और मानकीकृत प्रोटोकॉल स्थापित करना है. हम किसी भी सामान्य प्रश्न या समस्याओं को संबोधित करने की उम्मीद करते हैं जो मैन्युअल रूप से कोरॉइड प्लेक्सस को विभाजित करते समय उत्पन्न हो सकते हैं, विशेष रूप से मस्तिष्क के भीतर इसके स्थान की जटिल प्रकृति पर विचार करते हुए। इस प्रोटोकॉल को विकसित करने से एक बड़े पर्याप्त डेटा सेट का निर्माण होगा जिसमें जमीनी सच्चाई विभाजन मानचित्र शामिल होंगे जो हम तब एल्गोरिदम को प्रशिक्षित करने और विकसित करने के लिए उपयोग कर सकते हैं जो कोरॉइड प्लेक्सस को सटीक और स्वचालित रूप से विभाजित कर सकते हैं।भविष्य में, यह उन अध्ययनों को जन्म देगा जो विभिन्न न्यूरोलॉजिक और न्यूरोसाइकिएट्रिक विकारों के भीतर कोरॉइड प्लेक्सस की भूमिका की जांच करते हैं।

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.

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.