The work was funded by the National Research Foundation of Korea (JP as the PI). JK is funded by Kyungpook National University Research Fund; and MCVH is funded by the Row Fogo Charitable Trust and the Royal Society of Edinburgh. The hippocampal segmentation was adapted from in-house guidelines written by Dr. Karen Ferguson, at the Centre for Clinical Brain Sciences, Edinburgh, UK.

Brain MR images were acquired as per the protocol approved by the local institutional review board and ethics committee.
NOTE: The tools for shape modeling and analysis can be downloaded from the NITRC repository: <a href="https://www.nitrc.org/projects/dtmframework/" target="_blank">https://www.nitrc.org/projects/dtmframework/</a>. The GUI software (DTMModeling.exe) can be executed after extraction. See Figure 1.
1. Brain MR Image Segmentation
<ol>
	<li>Acquire brain MR images of individual subjects and brain segmentation masks. 
	NOTE: Usually, we acquire T1-weighted MR images for analyses of brain structures. We assume that the MR images are pre-processed for gradient non-linearity correction and intensity inhomogeneity correction using N311, improved N3 methods12, or FSL-FAST13. Some freely available tools for automatic segmentation of human brain structures are listed in Table 1.</li>
	<li>Correct the segmentation results manually. 
	NOTE: Open GUI software supporting manual segmentation are listed in Table 2. Manual segmentation protocols for the brain structures can be found here14,15,16. A video guide on manual segmentation for hippocampus is here17. We describe the protocol for hippocampal segmentation in the next section.
	<ol>
		<li>Open the T1-weighted MRI and the automatic segmentation results using the Open File menu.</li>
		<li>Load the Segmentation plugin by clicking Window Menu | Show | Segmentation.</li>
		<li>Correct the segmentation mask using the Add, Subtract, and Correction tools in the Segmentation plugin.</li>
		<li>Save the corrected segmentation mask in Nifti format using the Save menu.</li>
	</ol>
	</li>
</ol>
2. Manual Editing of Hippocampal Segmentation
NOTE: We introduce a protocol for manually editing of brain segmentation using the GUI modeling software based on the MITK workbench (<a href="http://www.mitk.org/" target="_blank">http://www.mitk.org/</a>). The MITK workbench provides various functions for the manual and automatic segmentation and medical image visualization. We demonstrate the manual editing process for the left and right hippocampi. Steps for manually editing18 the result of the automatic hippocampal segmentation are as follows.
<ol>
	<li>Open the T1-weighted MR image and the results of the automatic hippocampal segmentation using the MITK workbench software.</li>
	<li>Load the Segmentation plugin in the MITK workbench by clicking on the menu Window | Show View | Segmentation.</li>
	<li>Select the coronal view by clicking the right-hand side icon that appears in the top right-hand side corner of the Display window.</li>
	<li>Edit the binary mask of each hippocampus (i.e., left and right) in the coronal view, starting from the hippocampal head to the body as follows.
	<ol>
		<li>Scroll throughout the volume until the uncus is found. Include the uncus in the hippocampal mask where it is present.</li>
		<li>Edit the mask of the hippocampal body after the uncus has receded using the Add and Subtract function in the Segmentation plugin.</li>
		<li>Continue editing the hippocampal mask until the hippocampal tail is found. As the pulvinar nucleus of the thalamus recedes superior to the hippocampus, the fornix emerges.</li>
		<li>Finish editing the last coronal slice of the hippocampus in which the entire length of the fornix is visible but not yet continuous with the splenium of the corpus callosum. 
		NOTE: Cerebrospinal fluid (CSF) spaces can be contained within the hippocampal regions. The CSF spaces can be removed from the hippocampal masks using the Subtract tool in the segmentation plugin of the MITK workbench. it may be easier to define the hippocampal regions entirely and then go through all coronal slices from the hippocampal head to tail for the removal of CSF spaces.</li>
		<li>Follow the same process for editing the binary masks of both hippocampi. 
		NOTE: The Add, Subtract, and Correction tools of the Segmentation plugin in the MITK workbench can be used for the manual editing. The Correction tool is easy to handle small errors in the segmentation mask by performing addition and subtraction according to user input and the segmentation mask without additional tool selection.</li>
	</ol>
	</li>
	<li>Save the binary masks for left and right hippocampi in Nifti format (nii or nii.gz) using the Save menu in the MITK workbench software. 
	NOTE: The binary masks of left and right hippocampi should be saved separately for the subsequent hippocampal shape model steps.</li>
</ol>
3. Group Template Construction
NOTE: After the segmentation and manual editing for all subjects, the individual shape modeling requires the template model of the target structure. We construct the template model from the average binary mask for a population, acquired using &#34;ShapeModeling&#34; plugin in the MITK Workbench. Steps of the template model construction using GUI software are as follows.
<ol>
	<li>Load the ShapeModeling plugin using the menu function: Window | Show View | Shape Modeling.</li>
	<li>Open a directory containing the binary masks of a study population by clicking the Open Directory button in the ShapeModeling plugin.</li>
	<li>Click the Template Construction button in the ShapeModeling plugin.</li>
	<li>Check the mean shape mesh and save it in stereolithography (STL) format using the Save menu.</li>
</ol>
4. Individual Shape Reconstruction
NOTE: At this step, we perform the shape modeling for individual subjects using Start Shape Modeling button in the &#34;ShapeModeling&#34; plugin. We list the software parameters of this plugin in Table 3. Detailed explanation on each parameter can be found here5. Steps of the individual shape reconstruction using GUI software are as follows.
<ol>
	<li>Load T1-weighted MR image and its segmentation mask using the Open File menu. 
	NOTE: We use the T1-weighted MR image for visual validation.</li>
	<li>Check the modeling parameters in ShapeModeling plugin and modify if necessary. 
	NOTE: If the template model is not deformed or the distance between the template model and the image boundary is large, it is recommended to increase the boundary search range. If some geometric distortions are found, increasing maxAlpha and minAlpha with step 0.5 would help to resolve the issue. It is important to check the voxel intensity for the target object in the segmentation mask. If the value is not 1, intensity parameter should be changed accordingly.</li>
	<li>Click the Shape Modeling button to run the shape modeling process and check the result in the 3D view of MITK workbench.</li>
	<li>Repeat steps 4.2 and 4.3, when the template model is not fitted to the image boundary closely. 
	NOTE: The template model is visualized with the segmentation mask in the sagittal, coronal, axial, and 3D view of the MITK workbench. The template surface is not deformed when the distance between the template model and the image boundary is less than a threshold which is one tenth of the smallest voxel size.</li>
	<li>Save the modeling result in a stereolithography (STL) format using the Save menu in MITK framework.</li>
</ol>
5. Group-wise Shape Normalization and Shape Difference Measurement
NOTE: At this step, we align the individual shape models to the template model and compute the point-wise shape deformity between the corresponding vertices between the template model and the individual shape model. Steps for the shape deformity measurement are as follows.
<ol>
	<li>Select the shape model of a subject in the Data Manager of the MITK workbench. 
	NOTE: Users can select multiple models for the deformity measurement.</li>
	<li>Perform the deformity measurement by clicking the Measurement button in the ShapeModeling plugin.</li>
</ol>

<ol>
	<li>Costafreda, S. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+hippocampal+shape+analysis+predicts+the+onset+of+dementia+in+mild+cognitive+impairment.">Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment.</a> NeuroImage. 56 (1), 212-219 (2011).</li><li>Platero, C., Lin, L., Tobar, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Longitudinal+Neuroimaging+Hippocampal+Markers+for+Diagnosing+Alzheimer's+Disease.">Longitudinal Neuroimaging Hippocampal Markers for Diagnosing Alzheimer's Disease.</a> Neuroinformatics. , 1-19 (2018).</li><li>Vald&#233;s Hern&#225;ndez, M. D. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rationale,+design,+and+methodology+of+the+image+analysis+protocol+for+studies+of+patients+with+cerebral+small+vessel+disease+and+mild+stroke.">Rationale, design, and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke.</a> Brain and behavior. 5 (12), e00415 (2015).</li><li>Kalmady, S. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clinical+correlates+of+hippocampus+volume+and+shape+in+antipsychotic-na&#239;ve+schizophrenia.">Clinical correlates of hippocampus volume and shape in antipsychotic-na&#239;ve schizophrenia.</a> Psychiatry Research: Neuroimaging. 263, 93-102 (2017).</li><li>Kim, J., Vald&#233;s Hern&#225;ndez, M. D. C., Royle, N. A., Park, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hippocampal+Shape+Modeling+Based+on+a+Progressive+Template+Surface+Deformation+and+its+Verification.">Hippocampal Shape Modeling Based on a Progressive Template Surface Deformation and its Verification.</a> IEEE Transactions on Medical Imaging. 34 (6), 1242-1261 (2015).</li><li>Kim, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+shape+analysis+of+the+brain's+third+ventricle+using+a+midplane+encoded+symmetric+template+model.">3D shape analysis of the brain's third ventricle using a midplane encoded symmetric template model.</a> Computer Methods and Programs in Biomedicine. 129, 51-62 (2016).</li><li>Kim, J., Ryoo, H., Vald&#233;s Hern&#225;ndez, M. D. C., Royle, N. A., Park, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain+Ventricular+Morphology+Analysis+Using+a+Set+of+Ventricular-Specific+Feature+Descriptors.">Brain Ventricular Morphology Analysis Using a Set of Ventricular-Specific Feature Descriptors.</a> International Symposium on Biomedical Simulation. , 141-149 (2014).</li><li>Vald&#233;s Hern&#225;ndez, M. D. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hippocampal+morphology+and+cognitive+functions+in+community-dwelling+older+people:+the+Lothian+Birth+Cohort+1936.">Hippocampal morphology and cognitive functions in community-dwelling older people: the Lothian Birth Cohort 1936.</a> Neurobiology of Aging. 52, 1-11 (2017).</li><li>Lee, P., Ryoo, H., Park, J., Jeong, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Morphological+and+Microstructural+Changes+of+the+Hippocampus+in+Early+MCI:+A+Study+Utilizing+the+Alzheimer's+Disease+Neuroimaging+Initiative+Database.">Morphological and Microstructural Changes of the Hippocampus in Early MCI: A Study Utilizing the Alzheimer's Disease Neuroimaging Initiative Database.</a> Journal of Clinical Neurology. 13 (2), 144-154 (2017).</li><li>Cox, S. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Associations+between+hippocampal+morphology,+diffusion+characteristics,+and+salivary+cortisol+in+older+men.">Associations between hippocampal morphology, diffusion characteristics, and salivary cortisol in older men.</a> Psychoneuroendocrinology. 78, 151-158 (2017).</li><li>Sled, J. G., Zijdenbos, A. P., Evans, A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+nonparametric+method+for+automatic+correction+of+intensity+nonuniformity+in+MRI+data.">A nonparametric method for automatic correction of intensity nonuniformity in MRI data.</a> IEEE Transactions on Medical Imaging. 17 (1), 87-97 (1998).</li><li>Tustison, N. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=N4ITK:+improved+N3+bias+correction.">N4ITK: improved N3 bias correction.</a> IEEE Transactions on Medical Imaging. 29 (6), 1310-1320 (2010).</li><li>Zhang, Y., Brady, M., Smith, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segmentation+of+brain+MR+images+through+a+hidden+Markov+random+field+model+and+the+expectation-maximization+algorithm.">Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm.</a> IEEE Transactions on Medical Imaging. 20 (1), 45-57 (2001).</li><li>Wardlaw, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain+aging,+cognition+in+youth+and+old+age+and+vascular+disease+in+the+Lothian+Birth+Cohort+1936:+rationale,+design+and+methodology+of+the+imaging+protocol.">Brain aging, cognition in youth and old age and vascular disease in the Lothian Birth Cohort 1936: rationale, design and methodology of the imaging protocol.</a> International Journal of Stroke. 6 (6), 547-559 (2011).</li><li>Morey, R. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+automated+segmentation+and+manual+tracing+for+quantifying+hippocampal+and+amygdala+volumes.">A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes.</a> NeuroImage. 45 (3), 855-866 (2009).</li><li>Boccardi, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Survey+of+protocols+for+the+manual+segmentation+of+the+hippocampus:+preparatory+steps+towards+a+joint+EADC-ADNI+harmonized+protocol.">Survey of protocols for the manual segmentation of the hippocampus: preparatory steps towards a joint EADC-ADNI harmonized protocol.</a> Journal of Alzheimer's Disease. 26 (s3), 61-75 (2011).</li><li>Winterburn, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+In+Vivo+Manual+Segmentation+Protocol+for+Human+Hippocampal+Subfields+Using+3T+Magnetic+Resonance+Imaging.">High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging.</a> Journal of Visualized Experiments. (105), e51861 (2015).</li><li>MacLullich, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intracranial+capacity+and+brain+volumes+are+associated+with+cognition+in+healthy+elderly+men.">Intracranial capacity and brain volumes are associated with cognition in healthy elderly men.</a> Neurology. 59 (2), 169-174 (2002).</li><li>Gower, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Generalized+Procrustes+analysis.">Generalized Procrustes analysis.</a> Psychometrika. 40 (1), 33-51 (1975).</li><li>Lorensen, W. E., Cline, H. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Marching+cubes:+A+high+resolution+3D+surface+construction+algorithm.">Marching cubes: A high resolution 3D surface construction algorithm.</a> ACM Siggraph Computer Graphics. , 163-169 (1987).</li></ol>

The authors declare that there is no conflict of interest.

In summary, we have described the software pipeline for the shape analysis on brain structures including (1) MR image segmentation using open tools (2) individual shape reconstruction using a deformable template model, and (3) quantitative shape difference measurement via transitive shape correspondence with the template model. Statistical analysis under the false discovery rate (FDR) correction is performed with the shape deformity to investigate the significance of morphological changes of brain structures, associated ...

Shape analysis of brain structures has emerged as the preferred tool to investigate their morphological changes under pathological processes, such as neurodegenerative diseases and aging1. Various computational methods are required to 1) accurately delineate the boundaries of target structures from medical images, 2) reconstruct the target shape in the form of 3D surface mesh, 3) build inter-subjects correspondence across the individual shape models via shape parameterization or surface registration, and 4) quantitatively assess the regional shape differences between individuals or groups. Over the past several years, many methods have been introduced in neuroimaging studies for each of these steps. However, despite the remarkable developments in the field, there are not many frameworks immediately applicable to research. In this article, we describe each step of the shape analysis of brain structures using our custom shape modeling tools and publicly available image segmentation tools.
Here, we demonstrate the shape analysis framework for brain structures through the shape analysis of the left and right hippocampi using a dataset of adult controls and Alzheimer&#39;s disease patients. Atrophy of the hippocampi is recognized as a critical imaging biomarker in neurodegenerative diseases2,3,4. In our shape analysis framework, we employ the template model of the target structure and the template-to-image deformable registration in the shape modeling process. The template model encodes general shape characteristics of the target structure in a population, and it also provides a baseline for quantifying the shape differences among the individual models via their transitive relation with the template model. In the template-to-image registration, we have developed a Laplacian surface deformation method to fit the template model to the target structure in individual images while minimizing the distortion of the point distribution in the template model5,6,7. The feasibility and robustness of the proposed framework have been validated in recent neuroimaging studies of cognitive aging8, early detection of mild cognitive impairment9, and to explore associations between brain structural changes and cortisol levels10. This approach would make it easier to use the shape modeling and analysis methods in further neuroimaging studies.

three-dimensional shape modeling and analysis of brain structures

Statistical shape analysis of brain structures has been used to investigate the association between their structural changes and pathological processes. We have developed a software package for accurate and robust shape modeling and group-wise analysis. Here, we introduce an pipeline for the shape analysis, from individual 3D shape modeling to quantitative group shape analysis. We also describe the pre-processing and segmentation steps using open software packages. This practical guide would help researchers save time and effort in 3D shape analysis on brain structures.

The shape modeling process described here has been employed for various neuroimaging studies on aging6,8,10 and Alzheimer&#39;s disease5,9. Especially, this shape modeling method showed its accuracy and sensitivity in the shape analysis on the hippocampus for an aging population of 654 subjects8. A quantitative ana...

Watch this Scientific Journal Video about Three-Dimensional Shape Modeling and Analysis of Brain Structures at JoVE.com

Three-Dimensional Shape Modeling and Analysis of Brain Structures

We introduce a semi-automatic protocol for shape analysis on brain structures, including image segmentation using open software, and further group-wise shape analysis using an automated modeling package. Here, we demonstrate each step of the 3D shape analysis protocol with hippocampal segmentation from brain MR images.

Statistical shape analysis of brain structures has been used to investigate the association between their structural changes and pathological processes. ...

three-dimensional-shape-modeling-and-analysis-of-brain-structures

Research

JoVE Journal

Neuroscience

7.0K Views.  Kyungpook National University. Het nauwkeurig herstellen van de vormkenmerken tegen ruwe en luidruchtige segmentaties is van cruciaal belang voor het bereiken van een goede anatomische correspondentie tussen individuele hersenvormmodellen. Ons framework biedt verschillende tools voor individuele vormmodellering, groepswijze sjabloonconstructie en vormvervormingsberekening. En het is gebruikt voor grote datasets van het menselijk brein.Het aantonen van deze procedure zal Dr.Jaeil Kim, een voormalige grad student uit ...

Video: Three-Dimensional Shape Modeling and Analysis of Brain Structures

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

The ventricular system carries and circulates cerebral spinal fluid (CSF) and facilitates clearance of solutes and toxins from the brain. The functional units of the ventricles are ciliated epithelial cells termed ependymal cells, which line the ventricles and through ciliary action are capable of generating laminar flow of CSF at the ventricle surface. This monolayer of ependymal cells also provides barrier and filtration functions that promote exchange between brain interstitial fluids (ISF) and circulating CSF. Biochemical changes in the brain are thereby reflected in the composition of the CSF and destruction of the ependyma can disrupt the delicate balance of CSF and ISF exchange. In humans there is a strong correlation between lateral ventricle expansion and aging. Age-associated ventriculomegaly can occur even in the absence of dementia or obstruction of CSF flow. The exact cause and progression of ventriculomegaly is often unknown; however, enlarged ventricles can show regional and, often, extensive loss of ependymal cell coverage with ventricle surface astrogliosis and associated periventricular edema replacing the functional ependymal cell monolayer. Using MRI scans together with postmortem human brain tissue, we describe how to prepare, image and compile 3D renderings of lateral ventricle volumes, calculate lateral ventricle volumes, and characterize periventricular tissue through immunohistochemical analysis of en face lateral ventricle wall tissue preparations. Corresponding analyses of mouse brain tissue are also presented supporting the use of mouse models as a means to evaluate changes to the lateral ventricles and periventricular tissue found in human aging and disease. Together, these protocols allow investigations into the cause and effect of ventriculomegaly and highlight techniques to study ventricular system health and its important barrier and filtration functions within the brain.

Using MRI scans (human), 3D imaging software, and immunohistological analysis, we document changes to the brain&#8217;s lateral ventricles. Longitudinal 3D mapping of lateral ventricle volume changes and characterization of periventricular cellular changes that occur in the human brain due to aging or disease are then modeled in mice.

The ventricular system carries and circulates cerebral spinal fluid (CSF) and facilitates clearance of solutes and toxins from the brain. The functional ...

Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning

Magnetic resonance imaging (MRI) allows for the delineation between normal and abnormal tissue on a macroscopic scale, sampling an entire tissue volume three-dimensionally. While MRI is an extremely sensitive tool for detecting tissue abnormalities, association of signal changes with an underlying pathological process is usually not straightforward. In the central nervous system, for example, inflammation, demyelination, axonal damage, gliosis, and neuronal death may all induce similar findings on MRI. As such, interpretation of MRI scans depends on the context, and radiological-histopathological correlation is therefore of the utmost importance. Unfortunately, traditional pathological sectioning of brain tissue is often imprecise and inconsistent, thus complicating the comparison between histology sections and MRI. This article presents novel methodology for accurately sectioning primate brain tissues and thus allowing precise matching between histology and MRI. The detailed protocol described in this article will assist investigators in applying this method, which relies on the creation of 3D printed brain slicers. Slightly modified, it can be easily implemented for brains of other species, including humans.

The overall goal of this protocol is to accurately align magnetic resonance imaging (MRI) image volumes with histology sections via the creation of customized 3D-printed brain holders and slicer boxes.

Magnetic resonance imaging (MRI) allows for the delineation between normal and abnormal tissue on a macroscopic scale, sampling an entire tissue volume ...

Analysis of Craniomaxillofacial Malformations in Mice Using Three-dimensional Microcomputed Tomography

To model craniofacial malformations caused by vitamin A deficiency (VAD), we expressed a dominant-negative retinoid receptor mutation in osteoblasts to specifically inhibit RAR transcriptional activity in mice. This approach allowed us to investigate the effects of VAD on cranial hypomineralization, mandibular deformity, and clavicular hypoplasia in clinical cases. In this study, microcomputed tomography (microCT) scanning of the craniomaxillofacial region of mice represented a valuable tool for studying the growth and development of this animal model. The manual estimation of images is both time-consuming and inaccurate. Hence, here, we present a straightforward, efficient, and accurate approach for segmenting and quantifying the microCT images of each craniomaxillofacial bone. MicroCT software was used to slice the mandible, frontal bone, parietal bone, nasal bone, premaxilla, maxilla, interparietal bone, and occipital bone of mice and measure their corresponding lengths and widths. This segmentation method can be applied to study growth and development in developmental biology, biomedicine, and other related sciences and allows researchers to analyze the effects of genetic mutations on individual craniofacial bones.

Here, we describe in depth a method, which is based on microcomputed tomography, to segment and measure 3D models of craniomaxillofacial bones in mice, for better assessment of craniomaxillofacial bone development in mice than what is possible with current methods.

To model craniofacial malformations caused by vitamin A deficiency (VAD), we expressed a dominant-negative retinoid receptor mutation in osteoblasts to ...

Developmental Biology

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

In this paper, we outline a method for surgical preparation that allows for the practical planning of a variety of neurosurgeries in NHPs solely using data extracted from magnetic resonance imaging (MRI). This protocol allows for the generation of 3D printed anatomically accurate physical models of the brain and skull, as well as an agarose gel model of the brain modeling some of the mechanical properties of the brain. These models can be extracted from MRI using brain extraction software for the model of the brain, and custom code for the model of the skull. The preparation protocol takes advantage of state-of-the-art 3D printing technology to make interfacing brains, skulls, and molds for gel brain models. The skull and brain models can be used to visualize brain tissue inside the skull with the addition of a craniotomy in the custom code, allowing for better preparation for surgeries directly involving the brain. The applications of these methods are designed for surgeries involved in neurological stimulation and recording as well as injection, but the versatility of the system allows for future expansion of the protocol, extraction techniques, and models to a wider scope of surgeries.

The method outlined below aims to provide a comprehensive protocol for the preparation of nonhuman primate (NHP) neurosurgery using a novel combination of three-dimensional (3D) printing methods and MRI data extraction.

In this paper, we outline a method for surgical preparation that allows for the practical planning of a variety of neurosurgeries in NHPs solely using ...

Bioengineering

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Phantoms are essential tools for clinical training, surgical planning and the development of novel medical devices. However, it is challenging to create anatomically accurate head phantoms with realistic brain imaging properties because standard fabrication methods are not optimized to replicate any patient-specific anatomical detail and 3D printing materials are not optimized for imaging properties. In order to test and validate a novel navigation system for use during brain tumor surgery, an anatomically accurate phantom with realistic imaging and mechanical properties was required. Therefore, a phantom was developed using real patient data as input and 3D printing of molds to fabricate a patient-specific head phantom comprising the skull, brain and tumor with both ultrasound and X-ray contrast. The phantom also had mechanical properties that allowed the phantom tissue to be manipulated in a similar manner to how human brain tissue is handled during surgery. The phantom was successfully tested during a surgical simulation in a virtual operating room.
The phantom fabrication method uses commercially available materials and is easy to reproduce. The 3D printing files can be readily shared, and the technique can be adapted to encompass many different types of tumor.

This protocol describes the fabrication of a patient specific skull, brain and tumor phantom. It uses 3D printing to create molds, and polyvinyl alcohol (PVA-c) is used as the tissue mimicking material.

Phantoms are essential tools for clinical training, surgical planning and the development of novel medical devices. However, it is challenging to create ...

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy begins many years prior to the motor onset (during the &#34;premanifest&#34; period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, &#34;MRICloud&#34;, which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the &#34;change-point&#34;, with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.

This paper describes a statistical model for volumetric MRI data analysis, which identifies the &#34;change-point&#34; when brain atrophy begins in premanifest Huntington&#39;s disease. Whole-brain mapping of the change-points is achieved based on brain volumes obtained using an atlas-based segmentation pipeline of T1-weighted images.

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy ...

Medicine

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

The human brain, being a multiscale system, has both macroscopic electrical signals, globally flowing along thick white-matter fiber bundles, and microscopic neuronal spikes, propagating along axons and dendrites. Both scales complement different aspects of human cognitive and behavioral functions. At the macroscopic level, MRI has been the current standard imaging technology, in which the smallest spatial resolution, voxel size, is 0.1&#8211;1 mm3. Also, at the microscopic level, previous physiological studies were aware of nonuniform neuronal architectures within such voxels. This study develops a powerful way to accurately embed microscopic data into a macroscopic map by interfacing biological scientific research with technological advancements in 3D scanning technology. Since 3D scanning technology has mostly been used for engineering and industrial design until now, it is repurposed for the first time to embed microconnectomes into the whole brain while preserving natural spiking in living brain cells. In order to achieve this purpose, first, we constructed a scanning protocol to obtain accurate 3D images from living bio-organisms inherently challenging to image due to moist and reflective surfaces. Second, we trained to keep speed to prevent the degradation of living brain tissue, which is a key factor in retaining better conditions and recording more natural neuronal spikes from active neurons in the brain tissue. Two cortical surface images, independently extracted from two different imaging modules, namely MRI and 3D scanner surface images, surprisingly show a distance error of only 50 &#956;m as mode value of the histogram. This accuracy is comparable in scale to the microscopic resolution of intercellular distances; also, it is stable among different individual mice. This new protocol, the 3D novel embedding overlapping (3D-NEO) protocol, bridges macroscopic and microscopic levels derived by this integrative protocol and accelerates new scientific findings to study comprehensive connectivity architectures (i.e., microconnectome).

This article introduces an experimental protocol using 3D scanning technology bridging two spatial scales: the macroscopic spatial scale of whole-brain anatomy imaged by MRI at &#62;100 &#956;m and the microscopic spatial scale of neuronal distributions using immunohistochemistry staining and a multielectrode array system and other methods (~10 &#956;m).

The human brain, being a multiscale system, has both macroscopic electrical signals, globally flowing along thick white-matter fiber bundles, and ...

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction of high-resolution imaged stacks to reveal ultrastructural patterns that could not be resolved using 2D images. Indeed, the latter might lead to a misinterpretation of morphologies, like in the case of mitochondria; the use of 3D models is, therefore, more and more common and applied to the formulation of morphology-based functional hypotheses. To date, the use of 3D models generated from light or electron image stacks makes qualitative, visual assessments, as well as quantification, more convenient to be performed directly in 3D. As these models are often extremely complex, a virtual reality environment is also important to be set up to overcome occlusion and to take full advantage of the 3D structure. Here, a step-by-step guide from image segmentation to reconstruction and analysis is described in detail.

The described pipeline is designed for the segmentation of electron microscopy datasets larger than gigabytes, to extract whole-cell morphologies. Once the cells are reconstructed in 3D, customized software designed around individual needs can be used to perform a qualitative and quantitative analysis directly in 3D, also using virtual reality to overcome view occlusion.

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction ...

Automated 3D Brain and Skull Modeling from MRI for NHP Neurosurgical Planning

A Neural Implant Design Toolbox for Nonhuman Primates

This paper describes an in-house method of 3D brain and skull modeling from magnetic resonance imaging (MRI) tailored for nonhuman primate (NHP) neurosurgical planning. This automated, computational software-based technique provides an efficient way of extracting brain and skull features from MRI files as opposed to traditional manual extraction techniques using imaging software. Furthermore, the procedure provides a method for visualizing the brain and craniotomized skull together for intuitive, virtual surgical planning. This generates a drastic reduction in time and resources from those required by past work, which relied on iterative 3D printing. The skull modeling process creates a footprint that is exported into modeling software to design custom-fit cranial chambers and headposts for surgical implantation. Custom-fit surgical implants minimize gaps between the implant and the skull that could introduce complications, including infection or decreased stability. By implementing these pre-surgical steps, surgical and experimental complications are reduced. These techniques can be adapted for other surgical processes, facilitating more efficient and effective experimental planning for researchers and, potentially, neurosurgeons.

Author Spotlight: Streamlined Brain and Skull Modeling for Enhanced Neurosurgical Planning in NHP Research

This paper outlines automated processes for nonhuman primate neurosurgical planning based on magnetic resonance imaging (MRI) scans. These techniques use procedural steps in programming and design platforms to support customized implant design for NHPs. The validity of each component can then be confirmed using three-dimensional (3D) printed life-size anatomical models.

This paper describes an in-house method of 3D brain and skull modeling from magnetic resonance imaging (MRI) tailored for nonhuman primate (NHP) ...

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Three-dimensional (3D) printing technologies offer the possibility of visualizing patient-specific pathologies in a physical model of correct dimensions. The model can be used for planning and simulating critical steps of a surgical approach. Therefore, it is important that anatomical structures such as blood vessels inside a tumor can be printed to be colored not only on their surface, but throughout their whole volume. During simulation this allows for the removal of certain parts (e.g., with a high-speed drill) and revealing internally located structures of a different color. Thus, diagnostic information from various imaging modalities (e.g., CT, MRI) can be combined in a single compact and tangible object.
However, preparation and printing of such a fully colored anatomical model remains a difficult task. Therefore, a step-by-step guide is provided, demonstrating the fusion of different cross-sectional imaging data sets, segmentation of anatomical structures, and creation of a virtual model. In a second step the virtual model is printed with volumetrically colored anatomical structures using a plaster-based color 3D binder jetting technique. This method allows highly accurate reproduction of patient-specific anatomy as shown in a series of 3D-printed petrous apex chondrosarcomas. Furthermore, the models created can be cut and drilled, revealing internal structures that allow for simulation of surgical procedures.

The protocol describes the fabrication of fully colored three-dimensional prints of patient-specific, anatomical skull models to be used for surgical simulation. The crucial steps of combining different imaging modalities, image segmentation, three-dimensional model extraction, and production of the prints are explained.

Three-dimensional (3D) printing technologies offer the possibility of visualizing patient-specific pathologies in a physical model of correct dimensions. ...

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Samenvatting

Meer video's verkennen

Hoofdstukken in deze video