Three-Dimensional Shape Modeling and Analysis of Brain Structures
November 14th, 2019
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.
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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Overview
This study introduces a semi-automatic protocol for 3D shape analysis of brain structures, focusing on hippocampal segmentation from brain MRI images. The methodology involves open software for image segmentation followed by group-wise shape analysis using an automated modeling package.
Key Study Components
Area of Science
- Neuroscience
- Image Analysis
- Structural Brain Modeling
Background
- Accurate shape recovery is essential for anatomical correspondence in brain models.
- The framework includes tools for shape modeling and deformity computation.
- Used with large human brain datasets for various studies.
- The software was developed by Dr. Jaeil Kim and demonstrates user-friendly features.
Purpose of Study
- To demonstrate a procedure for hippocampal segmentation and shape analysis.
- To provide an automated framework for modeling individual and group brain shapes.
- To offer tools for statistical analysis of shape variations.
Methods Used
- The method utilizes a graphic user interface for MRI image and segmentation editing.
- The study focuses on hippocampal structures using T1-weighted magnetic resonance images.
- Users manually edit hippocampal segmentations and construct group templates.
- Statistical analysis is performed using shape deformity measurements.
- MATLAB code is provided for analysis at the project page.
Main Results
- The approach allows for precise shape modeling of the hippocampus and computation of shape deformities.
- Results demonstrate differences in hippocampal shape between groups with varying brain tissue volumes.
- Individual shape characteristics are restored while minimizing distortion during modeling.
- Visualization of aligned models and average shape deformity maps is included.
Conclusions
- This protocol enables effective shape analysis and modeling of hippocampal structures.
- The methodology enhances understanding of anatomical variations in brain research.
- Applications extend to clinical studies involving conditions like Alzheimer's disease and other structural anomalies.
Frequently Asked Questions
Executive Industry Relevance
Accurate 3D shape modeling of brain structures enables quantitative assessment of anatomical variations linked to pathological processes, supporting target validation in neurodegenerative disease research. This approach provides predictive confidence by linking structural biomarkers to disease mechanisms, facilitating go/no-go decisions in early discovery pipelines. The method enhances translational continuity from discovery through preclinical modeling by standardizing shape phenotyping across cohorts.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses by quantifying hippocampal shape changes associated with disease states.
- Operational Value: Supports biological de-risking through reproducible shape deformity measurements across large datasets.
- Predictive Value: Improves portfolio triage by identifying structural biomarkers with strong genotype-phenotype correlations.
Screening & Assay Development
- Assay Readiness: Prepares validated biological systems for downstream screening by generating standardized shape models from MRI data.
- Quantitative Output: Delivers numerical shape deformity metrics enabling high-content screening of compound effects on brain structure.
- Platform Reuse: Facilitates scalable application across studies via automated template construction and open software integration.
Translational & Preclinical Research
- Disease Relevance: Directly applicable to Alzheimer's disease and aging studies where hippocampal atrophy is a key pathological feature.
- Translational Biomarker: Shape deformity maps serve as quantifiable endpoints for preclinical-to-clinical continuity.
- Risk-Adjusted Advancement: Enables objective comparison of intervention effects on brain structure, reducing failure risk in later stages.
Pipeline & Workflow Integration
The method integrates into the discovery continuum from early target validation through preclinical efficacy testing by providing standardized, quantifiable neuroanatomical phenotyping.
- Discovery Biology: Supports hypothesis testing by linking genetic or pharmacological interventions to measurable shape changes in hippocampal subfields.
- Screening: Enables assay standardization through group-wise template construction, ensuring consistent baseline comparisons across compound libraries.
- Analytics: Generates shape deformity vectors and statistical maps that allow objective comparison of structural phenotypes between treatment and control groups.
- Translational Research: Connects discovery findings to preclinical validation by preserving anatomical fidelity across species-specific adaptations of the framework.
- Enterprise Reuse: Functions as a reusable neuroimaging platform due to its open software foundation and modular workflow for shape modeling and analysis.
Operational & Enterprise Impact
- Scientific Value: Reduces mechanistic ambiguity by providing direct, quantitative links between structural brain changes and disease models.
- Operational Value: Ensures reproducibility and scalability through automated group template construction and standardized segmentation protocols.
- Strategic Value: Improves capital efficiency by enabling early detection of ineffective compounds based on lack of structural target engagement.
- Portfolio Impact: Supports risk-adjusted prioritization by identifying compounds that normalize shape deformity in disease-relevant brain circuits.
Implementation Considerations
- Requires expertise in neuroanatomy and MRI image interpretation for accurate manual segmentation correction.
- Depends on access to T1-weighted MRI data and computational infrastructure capable of handling large 3D shape datasets.
- Necessitates cross-team standardization of segmentation protocols to ensure shape comparability across sites and studies.
- Involves adaptation considerations when applying the framework to non-hippocampal brain structures or alternative imaging modalities.
- Limited by the need for user confirmation in key steps such as intensity parameter tuning, which may introduce variability if not standardized.
