Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
Summary
We present a workflow for segmenting and quantifying trabecular bones for 2D and 3D images based on the bone's outer boundary using an ImageJ plugin. This approach is more efficient and accurate than the current manual hand-contouring approach, and provides layer-by-layer quantifications, which are not available in current commercial software.
Abstract
Micro-computed tomography (micro-CT) is routinely used to assess bone quantity and trabecular microstructural properties in small animals under different bone loss conditions. However, the standard approach for trabecular analysis of micro-CT images is slice-by-slice semi-automatic hand-contouring, which is labor intensive and error prone. Described here is an efficient method for automatic segmentation of trabecular bones according to the bone’s outer boundaries, where trabecular bones can be identified and segmented automatically with accuracy with less operator bias when appropriate segmentation parameters are set. To profile satisfactory segmentation parameters, an image stack of segmentation results is displayed, where all possible combinations of the segmentation parameters are changed one by one in sequence, and segmentation results with associated parameters can easily be visually checked. As a quality-control feature of the plugin, simulated standard objects are quantified where the measured quantities can be compared with theoretical values. Layer-by-layer quantification of trabecular properties and trabecular thicknesses are reported by such a plugin, and the distributions of such properties within the selected regions can be profiled easily. Although layer-by-layer quantification retains more information about trabecular bones and facilitates further statistical analysis of structural changes, such measures are unavailable from the output of current commercial software, where only a single quantified value for each parameter is reported for each sample. Therefore, the described workflows are better approaches for analyzing trabecular bones with accuracy and efficiency.
Introduction
Micro-CT analysis of trabecular bones is the standard approach for tracking morphological changes of the bones in small animals under different bone loss conditions1,2,3, where several variables related to the structures of the bones are reported4. However, such parameters are not evenly distributed in the metaphysis of long bones5, and only a summarized or averaged value is reported for each structural variable of each sample by current commercial micro-CT machines6,7, though a single value cannot fully represent the characteristics of the measured parameter in the analyzing region. Layer-by-layer quantification of trabecular bones not only retains more information for each variable, but also enables the profiling of the distributions of such variables in the analyzing region, facilitating subsequent statistical analysis of structural changes under different conditions5. Therefore, the goal of this method is quantifying trabecular bones of micro-CT scans at each slice level, which is not available in any commercially available micro-CT analysis package currently.
To efficiently segment trabecular bones slice-by-slice, automatic segmentation methods are desirable. However, the current standard technique for micro-CT analysis is based on manual interactive contouring followed by semiautomatic interpolation to separate trabecular bones from the cortical compartments, which is labor intensive, error-prone, and associated with substantial operator bias8,9,10. Automatic segmentation methods11,12 were reported, but such methods are only optimal in regions with good separation between trabecular bones and cortical bones, but not in regions without clear separations. Moreover, different segmentation parameters are required for different samples12, and it is tedious to manually select satisfactory segmentation parameters applicable to groups of bone samples by trying various parameter combinations12, even though the segmentation process is automatic when all related parameters are set. As the bone outer boundary has the greatest contrast with the scanning background and the metaphyseal cortical shells of long bones show few changes in the chosen analyzing region, segmentation methods according to the outer-boundary contour of long bones can reliably and accurately separate trabecular bones from cortical shells. The advantage of such a segmentation method is that the segmentation is based on the difference between the background and the bone's outer boundary, but not on the differences between trabecular and cortical bones6,12,13, therefore it is generally easy to find a combination of segmentation parameters that is satisfactory for a group of bone samples, facilitating more reliable analysis of trabecular changes between different groups.
At each slice level, area, perimeter, and two-dimensional (2D) thickness are reported for 2D analysis, while volume, surface, and three-dimensional (3D) thickness are reported in 3D quantifications. Such information is generally not reported by current image analysis tools, indicating that the reported procedures can be applied to general images where such information is desired.
Protocol
Representative Results
Discussion
This study describes an ImageJ plugin for analyzing trabecular bones, which is automatic, efficient, and user friendly. The plugin can also be used to quantify any 2D or 3D object for layer-by-layer measures of areas, volumes, and thicknesses. Currently, only a single measured value for each trabecular parameter is reported for each sample by standard micro-CT analysis, which cannot fully represent the characteristics of the measured entity in the selected analyzing region. The described plugin reports layer-by-layer qua…
Offenlegungen
The authors have nothing to disclose.
Acknowledgements
This work was partially supported by grant NFSC 81170806. The authors would like to thank the micro-CT core facility of School of Stomatology, Wuhan University for helping scan and analyze the rat femurs.
Materials
| ImageJ | NIH | imagej | Any version with a java 1.8 run time |
| trabecular analysis plugin | Bomomics | bomomics | free or commercial version |
| Micro CT scanner | Scanco | μ-50 | micro CT from any vendor |
| Computer System | Lenovo | any brand | |
| Windows Operating System | Microsoft | Windows 7 x64 | any 64-bit Windows operating system |
| Office Software | Microsoft | Office 2010 | any speadsheet software that has xy chart function |
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