Three-Dimensional Shape Modeling and Analysis of Brain Structures

Summary

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.

Abstract

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.

Introduction

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 diseases^2,3,4. 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 model^5,6,7. 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.

Protocol

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

1. 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.
2. 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 here^14,15,16. A video guide on manual segmentation for hippocampus is here¹⁷. We describe the protocol for hippocampal segmentation in the next section.
   1. Open the T1-weighted MRI and the automatic segmentation results using the Open File menu.
   2. Load the Segmentation plugin by clicking Window Menu | Show | Segmentation.
   3. Correct the segmentation mask using the Add, Subtract, and Correction tools in the Segmentation plugin.
   4. Save the corrected segmentation mask in Nifti format using the Save menu.

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.

1. Open the T1-weighted MR image and the results of the automatic hippocampal segmentation using the MITK workbench software.
2. Load the Segmentation plugin in the MITK workbench by clicking on the menu Window | Show View | Segmentation.
3. Select the coronal view by clicking the right-hand side icon that appears in the top right-hand side corner of the Display window.
4. 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.
   1. Scroll throughout the volume until the uncus is found. Include the uncus in the hippocampal mask where it is present.
   2. Edit the mask of the hippocampal body after the uncus has receded using the Add and Subtract function in the Segmentation plugin.
   3. 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.
   4. 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.
   5. 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.
5. 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.

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.

1. Load the ShapeModeling plugin using the menu function: Window | Show View | Shape Modeling.
2. Open a directory containing the binary masks of a study population by clicking the Open Directory button in the ShapeModeling plugin.
3. Click the Template Construction button in the ShapeModeling plugin.
4. Check the mean shape mesh and save it in stereolithography (STL) format using the Save menu.

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.

1. 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.
2. 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.
3. Click the Shape Modeling button to run the shape modeling process and check the result in the 3D view of MITK workbench.
4. 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.
5. Save the modeling result in a stereolithography (STL) format using the Save menu in MITK framework.

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.

1. 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.
2. Perform the deformity measurement by clicking the Measurement button in the ShapeModeling plugin.

Results

The shape modeling process described here has been employed for various neuroimaging studies on aging^6,8,10 and Alzheimer's disease^5,9. 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 analysi...

Discussion

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 ...