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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. 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.
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'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.
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: https://www.nitrc.org/projects/dtmframework/. The GUI software (DTMModeling.exe) can be executed after extraction. See Figure 1.
1. Brain MR Image Segmentation
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 (http://www.mitk.org/). 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.
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 "ShapeModeling" plugin in the MITK Workbench. Steps of the template model construction using GUI software are as follows.
4. Individual Shape Reconstruction
NOTE: At this step, we perform the shape modeling for individual subjects using Start Shape Modeling button in the "ShapeModeling" 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.
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.
The shape modeling process described here has been employed for various neuroimaging studies on aging6,8,10 and Alzheimer'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...
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 ...
The authors declare that there is no conflict of interest.
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.
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