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.

Procedures involving animal subjects were conducted in accordance with the Guide for the Care and Use of Laboratory Animals (NIH publication, 8th edition, 2011), and have been reviewed and approved by the Institutional Animal Care and Use Committee of Wuhan University.
1. Software Installation
<ol>
	<li>Install ImageJ software. Download the Windows version of the ImageJ (version 1.51p) software bundled with 64-bit Java from https://imagej.nih.gov/ij/. Extract the downloaded software into a f...

<ol>
	<li>Ruegsegger, P., Koller, B., Muller, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+microtomographic+system+for+the+nondestructive+evaluation+of+bone+architecture.">A microtomographic system for the nondestructive evaluation of bone architecture.</a> Calcif Tissue Int. 58 (1), 24-29 (1996).</li><li>Muller, R., Ruegsegger, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Micro-tomographic+imaging+for+the+nondestructive+evaluation+of+trabecular+bone+architecture.">Micro-tomographic imaging for the nondestructive evaluation of trabecular bone architecture.</a> Stud Health Technol Inform. 40, 61-79 (1997).</li><li>Clark, D. P., Badea, C. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Micro-CT+of+rodents:+state-of-the-art+and+future+perspectives.">Micro-CT of rodents: state-of-the-art and future perspectives.</a> Phys Med. 30 (6), 619-634 (2014).</li><li>Bart, Z., Wallace, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microcomputed+Tomography+Applications+in+Bone+and+Mineral+Research.">Microcomputed Tomography Applications in Bone and Mineral Research.</a> Advances in Computed Tomography. 2, 121-127 (2013).</li><li>Ji, Y., Ke, Y., Gao, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intermittent+activation+of+notch+signaling+promotes+bone+formation.">Intermittent activation of notch signaling promotes bone formation.</a> Am J Transl Res. 9 (6), 2933-2944 (2017).</li><li>Jiang, Y., Zhao, J., White, D. L., Genant, H. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Micro+CT+and+Micro+MR+imaging+of+3D+architecture+of+animal+skeleton.">Micro CT and Micro MR imaging of 3D architecture of animal skeleton.</a> J Musculoskelet Neuronal Interact. 1 (1), 45-51 (2000).</li><li>Laib, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+micro-computed+tomography+of+trabecular+and+cortical+bone+architecture+with+application+to+a+rat+model+of+immobilisation+osteoporosis.">3D micro-computed tomography of trabecular and cortical bone architecture with application to a rat model of immobilisation osteoporosis.</a> Med Biol Eng Comput. 38 (3), 326-332 (2000).</li><li>Cole, H. A., Ichikawa, J., Colvin, D. C., O'Rear, L., Schoenecker, J. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+intra-osseous+growth+of+osteosarcoma+in+a+murine+model+with+radiographic+analysis.">Quantifying intra-osseous growth of osteosarcoma in a murine model with radiographic analysis.</a> J Orthop Res. 29 (12), 1957-1962 (2011).</li><li>Jensen, M. M., Jorgensen, J. T., Binderup, T., Kjaer, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tumor+volume+in+subcutaneous+mouse+xenografts+measured+by+microCT+is+more+accurate+and+reproducible+than+determined+by+18F-FDG-microPET+or+external+caliper.">Tumor volume in subcutaneous mouse xenografts measured by microCT is more accurate and reproducible than determined by 18F-FDG-microPET or external caliper.</a> BMC Med Imaging. 8, 16 (2008).</li><li>Soviero, V. M., Leal, S. C., Silva, R. C., Azevedo, R. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validity+of+MicroCT+for+in+vitro+detection+of+proximal+carious+lesions+in+primary+molars.">Validity of MicroCT for in vitro detection of proximal carious lesions in primary molars.</a> J Dent. 40 (1), 35-40 (2012).</li><li>Kohler, T., Stauber, M., Donahue, L. R., Muller, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+compartmental+analysis+for+high-throughput+skeletal+phenotyping+in+femora+of+genetic+mouse+models.">Automated compartmental analysis for high-throughput skeletal phenotyping in femora of genetic mouse models.</a> Bone. 41 (4), 659-667 (2007).</li><li>Buie, H. R., Campbell, G. M., Klinck, R. J., MacNeil, J. A., Boyd, S. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+segmentation+of+cortical+and+trabecular+compartments+based+on+a+dual+threshold+technique+for+in+vivo+micro-CT+bone+analysis.">Automatic segmentation of cortical and trabecular compartments based on a dual threshold technique for in vivo micro-CT bone analysis.</a> Bone. 41 (4), 505-515 (2007).</li><li>Dougherty, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+CT+in+the+measurement+of+bone+quantity+and+bone+quality+for+assessing+osteoporosis.">Quantitative CT in the measurement of bone quantity and bone quality for assessing osteoporosis.</a> Med Eng Phys. 18 (7), 557-568 (1996).</li><li>Doube, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BoneJ:+Free+and+extensible+bone+image+analysis+in+ImageJ.">BoneJ: Free and extensible bone image analysis in ImageJ.</a> Bone. 47 (6), 1076-1079 (2010).</li><li>Bouxsein, M. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Guidelines+for+assessment+of+bone+microstructure+in+rodents+using+micro-computed+tomography.">Guidelines for assessment of bone microstructure in rodents using micro-computed tomography.</a> J Bone Miner Res. 25 (7), 1468-1486 (2010).</li></ol>

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...

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&#39;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.

<table border="0" cellpadding="0" cellspacing="0" style="width:595px;" width="595">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">ImageJ</td>
			<td style="width:93px;">NIH</td>
			<td style="width:119px;">imagej</td>
			<td style="width:167px;">Any version with a java 1.8 run time</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">trabecular analysis plugin</td>
			<td style="width:93px;">Bomomics</td>
			<td style="width:119px;">bomomics</td>
			<td>free or commercial version</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Micro CT scanner</td>
			<td style="width:93px;">Scanco</td>
			<td style="width:119px;">&#956;-50</td>
			<td>micro CT from any vendor</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Computer System</td>
			<td style="width:93px;">Lenovo</td>
			<td style="width:119px;"></td>
			<td>any brand</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Windows Operating System</td>
			<td style="width:93px;">Microsoft</td>
			<td style="width:119px;">Windows 7 x64</td>
			<td>any 64-bit Windows operating system&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Office Software</td>
			<td style="width:93px;">Microsoft</td>
			<td style="width:119px;">Office 2010</td>
			<td>any speadsheet software that has xy chart function</td>
		</tr>
	</tbody>
</table>

outer-boundary assisted segmentation and quantification of trabecular bones by an imagej plugin

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.

The trabecular analysis plugin is designed to automatically segment and quantify trabecular bones with accuracy. Initially, bone outer boundary is detected and delineated followed by a hole-filling operation where any holes within bone outer cortical shells are filled. Then an erosion operation is performed to exclude the outer cortical bones and get the segmented trabecular bones. Finally, measures of trabecular bones in the segmented region are quantified.
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Watch this Scientific Journal Video about Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin at JoVE.com

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

We present a workflow for segmenting and quantifying trabecular bones for 2D and 3D images based on the bone&#39;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.

Micro-computed tomography (micro-CT) is routinely used to assess bone quantity and trabecular microstructural properties in small animals under different ...

The overall goal of this procedure is to quantify structural measurements of trabecular bones automatically with accuracy and efficiency. This method can help answer key questions in the image analysis field, such as how to measure irregular structures accurately with efficiency. The main advantage of this technique is that structural measurements of two-dimensional or three-dimensional objects by such a technique are more accurate and efficient than the current available quantification approaches.To begin this procedure, first install the ImageJ software and trabecular analysis plugins, as outlined in the text protocol. Open the ImageJ software. Under Plugins, BoMomics, Simulate Objects, click the Circle button.In the resulting popup window, enter 200 as the diameter, and then click OK to generate a simulated circle with a diameter of 200 pixels. Save the generated circle in TIFF format. Next, in Plugins, BoMomics, Simulate Objects, click the Square button.In the popup, enter 200 and the side length, and click OK to generate a simulated square with a side length of 200 pixels. Save it in TIFF format. Then, in the same menu, click the Sphere button.Enter 30 as the diameter, and click OK to generate the simulated sphere. Click the Plugins, 3D, Volume Viewer to view the sphere, and save it in TIFF format. Finally, in Plugins, BoMomics, Simulate Objects, click the Cylinder button.Enter 30 as the diameter and 100 as the height, and then click OK to generate the simulated cylinder. After this, click Plugins, 3D, Volume Viewer to view the cylinder, and save it in TIFF format. First, open the ImageJ software, and open or import a scanned image.Slide the bottom scroll bar to choose a slice, and then click the Image, Adjust, Threshold button. In the Threshold popup window, adjust the minimum and maximum threshold values to ensure that the bones are well separated from the background. Record the minimal threshold value as the cortical bone threshold value.Next, click the Plugins, BoMomics, Trabecular Parameter Profiling button. In the popup window, set the Slice Index to the position of the representative slice. Set the Cortical Bone, Range, and Step values to calculate a set of cortical thresholds for profiling segmentation parameters.After this, set the Noise Diameter, Step, and Range values to specify a set of noise values for the analysis. Set the Hole Diameter, Step, and Range values for calculating a set of hole values. Click OK to perform the parameter profiling.In the Parameter Profiling Results window, visually check the segmentation results, and select a slice layer where the bone's outer boundary is outlined accurately. Then, retrieve the profiling parameters from the corresponding entry in the Parameter Profiling Results table. To begin analyzing the trabecular bones, open the ImageJ software and open or import a scanned image.Click the Plugins, BoMomics, Trabecular Segmentation button. Fill in appropriate analysis parameters, as shown here. Next, click OK to perform the trabecular segmentation.Visually inspect the results in the Trabecular Segmentation Results window. Then, save the extracted trabecular bones shown in the Segmented Trabecular Bones window in TIFF format for further analysis. After this, click the Plugins, BoMomics, Trabecular Analysis button, and fill in the appropriate analysis parameters, as outlined in the text protocol.In the Results Reporting section, select one or more parameters to be measured where the options are trabecular bone volume, total volume, and thickness measured either two-dimensionally or three-dimensionally. To perform trabecular analysis, select the checkboxes for both 2D and 3D, and then click OK.To begin quantifying objects, open a simulated image in the ImageJ software. Select the Plugins, BoMomics, Trabecular Analysis button, and fill in the appropriate analysis parameters, keeping the default values for Start, End, Outline Boundary, and Trabecular Bones, while setting Noise Reduction Diameter, Hole Filling Diameter, and Cortical Thickness Diameter to zero.In the Results Reporting section, select both 2D and 3D as the parameters to be measured. Then, click OK to perform trabecular analysis on the simulated object. After this, calculate the calibrated bone volume, total volume, bone mineral content, bone volume fraction, and bone mineral density in spreadsheet columns, and analyze the data, as outlined in the text protocol.In this study, an ImageJ plugin is used to automatically segment and quantify trabecular bones. Representative parameter profiling analysis using different parameter conditions reveals that some combinations are more accurate when delineating a bone's outer boundaries than others. Next, segmentation and analysis is performed to quantify measurements of the trabecular bones.The results of this segmentation can be visually checked slice-by-slice. The raw quantifications of the bone volume, the total volume, the sum of gray values, and the thickness, either in two or three dimensions, can be reported depending on which options are selected during analysis. The calibration information is extracted from the scanned micro-CT dataset, and calibrated measurements of bone volume, total volume, bone mineral content, bone volume fraction, and bone mineral density are then calculated.Their distributions can then be profiled in the selected analyzing region, layer-by-layer, against the layer positions. Once mastered, this technique can quantify trabecular bones of 500 image layers in 10 to 20 minutes if it's performed properly. We first had the idea for this method when we found that the results reported by the micro-CT vendor software were not reproducible when the same dataset was analyzed by the same experienced operator.After watching this video, you should have a good understanding of how to choose appropriate bone segmentation parameters and perform analysis of long bones with minimal user interactions.

outer-boundary-assisted-segmentation-quantification-trabecular-bones

Research

JoVE Journal

Bioengineering

9.1K visualizações.  Wuhan University. The overall goal of this procedure is to quantify structural measurements of trabecular bones automatically with accuracy and efficiency. This method can help answer key questions in the image analysis field, such as how to measure irregular structures accurately with efficiency. The main advantage of this technique is that structural measurements of two-dimensional or three-dimensional objects by such a technique are more accurate and efficient than the current available quantification appro...