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.<!--The publication list on the shape modeling tools can be found in the proje...

<ol>
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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. ...

Accurately recovering the shape features against rough and noisy segmentations is critical to achieving good anatomical correspondence between individual brain shape models. Our framework provides various tools for individual shape modeling, group-wise template construction, and shape deformity computation. And it has been used for large datasets of the human brain.Demonstrating this procedure will be Dr.Jaeil Kim, a former grad student from my laboratory who developed the software for the brain shape modeling. For manual editing of the hippocampal segmentation, open the T1 weight magnetic resonance image and the automatic hippocampal segmentation results in the graphic user interface software. Click the icon in the display window to select the coronal view and scroll through the volume until the uncus is located.Including the uncus in the hippocampal mask where it is present, use the add and subtract functions to edit the mask of the hippocampal body after the uncus has receded. 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 will emerge.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. Then, save the masks for both the left and right hippocampi in NifTI format. To construct a group template model load the shape modeling plug-in and click Open Directory to open a directory containing the binary masks of the study population of interest.Input the desired number of vertices and click Template Construction for the group template construction. Then check the mean shape mesh. For individual shape reconstruction, load the T1-weighted magnetic resonance image of interest and its corresponding segmentation mask and select the working directory to save the files.Select a template model for the individual shape modeling and check and modify the modeling parameters in the shape modeling plug-in as necessary. So our modeling framework is almost automated, however, some steps require user confirmation. For example, if the value for the hippocampal regions is not one users must change the intensity parameter.Then, check the result in the 3D view of the toolkit workbench. To perform a shape and deformity measurement select the shape model of the subject of interest in the data manager of the software and click Select Template to select the template of interest to obtain the measurement. Here, a representative deformation of the hippocampal template model for individual shape reconstruction can be observed.The method induces a large to small scale deformation of the template model to minimize the distortion of its point distribution while restoring the individual shape characteristics. In this figure, reconstructed shape models from two subjects with their segmentation masks are shown. In these images aligned individual shape models, their average model, and the shape difference vectors with an individual shape model can be observed.These data represent average shape deformity maps projected onto the average model. For one group with a small brain tissue volume, and one group with a large brain tissue volume. The shape deformity maps of the two groups present opposite patterns of hippocampal shape difference at corresponding regions.Check the segmentation mask and the individual or either the shared model together. If the model is not fitted to the image boundary, adjust the modeling parameters to achieve better results. Statistical analysis using the shape deformity can be performed to investigate the group-wise shape We have also provided MATLAB code for the analysis at our project page.This robust method has been applied to a number of clinical studies, not only involving critical structure modeling such as Alzheimer's disease or aging study, but also foot-bone disorders which require the analysis of compound bones.

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6.9K vues.  Kyungpook National University. Il est essentiel de récupérer avec précision les caractéristiques de forme par rapport aux segmentations rugueuses et bruyantes pour obtenir une bonne correspondance anatomique entre les modèles individuels de forme du cerveau. Notre cadre fournit divers outils pour la modélisation des formes individuelles, la construction de modèles en groupe et le calcul de la déformation de la forme. Et il a été utilisé pour de grands ensembles de données du cerveau humain.Démontrant ce...