Name: outer-boundary assisted segmentation and quantification of trabecular bones by an imagej plugin
Uploaded: 2018-03-14T14:00:00.000+00:00
Duration: 9 min 36 s
Description: Watch this Scientific Journal Video about Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin at JoVE.com

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 folder, which will be subsequently referred to as the &#34;ImageJ Directory&#34;. 
	NOTE: Trabecular analysis plugins require 64-bit java run time (version 1.8) and a 64-bit windows operating system, preferably a 64-bit Windows 7 operating system.</li>
	<li>Install trabecular analysis plugins. Request trabecular analysis plugins from http://www.bomomics.com, and unzip the contents into ImageJ&#39;s plugins directory, which is &#34;ImageJ Directory/plugins&#34;. 
	NOTE: The plugin can be obtained either as a free version, where the measurements for 5 specified adjacent slices are reported, or as a commercial version, where a range of slices can be specified and measured.</li>
</ol>
2. Prepare 3D Dataset for Trabecular Analysis
<ol>
	<li>Scan rat femurs with a micro-CT machine following the standard scanning protocol5 and then save the data in a format that can be imported into ImageJ, e.g., tiff format. If multiple bone samples were scanned simultaneously in a single sample tube, first import the data into ImageJ, and then separate each individual bone by cropping off other samples using ImageJ&#39;s image processing tools. Subsequently, save the resulting image in a format that can be imported into ImageJ later. 
	NOTE: A representative sample image file used in the analysis is included (Supplementary File 1).</li>
	<li>Simulate standard 2D and 3D objects.
	<ol>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Circle button, and enter 200 as the diameter in the popup window, then click OK to generate the simulated circle with a diameter of 200 pixels (Figure 3B). Save the generated circle in tiff format.</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Square button, and enter 200 as the side length in the popup window, then click OK to generate the simulated square with a side length of 200 pixels (Figure 3B). Save the generated square in tiff format.</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Rectangle button, and enter 200 as the width and 100 as the height in the popup window, then click OK to generate the simulated rectangle with a width of 200 pixels and a height of 100 pixels (Figure 3B). Save the generated rectangle in tiff format. 
		NOTE: A circle (diameter: 200 pixels), a square (side length: 200 pixels), and a rectangle (width: 200 pixels; height: 100 pixels) are saved for the subsequent analyses.</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Cube button, and enter 30 as the side length in the popup window, then click OK to generate the simulated cube. Finally, click the Plugins | 3D | Volume Viewer to view the generated cube and save it in tiff format (Figure 3C).</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Cuboid button, and enter 80 as the length, 40 as the width, and 30 as the height in the popup window, then click OK to generate the simulated cuboid. Finally, click the Plugins | 3D | Volume Viewer to view the generated cuboid and save it in tiff format (Figure 3C).</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Sphere button, and enter 30 as the diameter in the popup window, then click OK to generate the simulated sphere. Finally, click the Plugins | 3D | Volume Viewer to view the generated sphere and save it in tiff format (Figure 3C).</li>
		<li>Open ImageJ software. Under the Plugins | BoMomics | Simulate Objects menu, click the Cylinder button, and enter 30 as the diameter and 100 as the height in the popup window, then click OK to generate the simulated cylinder. Finally, click the Plugins | 3D | Volume Viewer to view the generated cylinder and save it in tiff format (Figure 3C). 
		NOTE: A sphere (diameter: 30 pixels), a cube (side length: 30 pixels), a cuboid (length: 80 pixels; width: 40 pixels; height: 30 pixels), and a cylinder (diameter: 30 pixels; height: 100 pixels) are saved for the subsequent analyses.</li>
	</ol>
	</li>
</ol>
3. Profiling Analysis Parameters
<ol>
	<li>Open ImageJ software and open or import a scanned image.</li>
	<li>Slide the bottom scrollbar to choose one slice, then click the Image | Adjust | Threshold button. In the popup Threshold window, adjust the minimum and maximum threshold values by manual inspection to ensure that bones are well separated from the background, and record the minimal threshold value as the cortical bone threshold value. 
	NOTE: As different micro-CT manufacturers use different scaling factors for storing X-ray attenuation coefficients in the images, the actual threshold value should be determined empirically either by manual inspection or following the specific manufacturer&#39;s recommendations. In our practice, setting the minimum threshold for ISQ files produced by micro-CT machines to 6,000-7,000 can reliably separate bones from the scanning background.</li>
	<li>Click the Plugins | BoMomics | Trab Param Profiling button. In the popup window, set Slice Index to the position of the representative slice, and set the cortical bone (&#34;Cort Bone&#34;), range (&#34;Range&#34;), and step (&#34;Step&#34;) values for calculating a set of cortical thresholds for profiling segmentation parameters, where the cortical bone threshold value is acquired by step 3.2 (Figure 1). The default range and step values of 2,000 and 400 work for most micro-CT images. Set the Slice Index to 5 for the example dataset, and keep the default values for other settings. 
	Note: &#34;Cort Bone&#34; is the threshold for cortical bones, and the threshold value used for parameter profiling changes from the lowest threshold value to the highest threshold value with the increment of Step value, where the lowest threshold value is the value of Cort Bone - Range, and the highest threshold value is Cort Bone + Range. If the set values for Cort Bone, Range, and Step are 6,000, 1,000, and 500, respectively, then the lowest threshold value for cortical bones is 6,000-1,000=5,000, the highest threshold value is 6,000+1,000=7,000, and the thresholds used in the analyses are 5,000, 5,500, 6,000, 6,500, and 7,000.</li>
	<li>Set noise diameter (&#34;Noise Dia.&#34;), step (&#34;Step&#34;), and range (&#34;Range&#34;) values for specifying a set of noise values in the analyses, and the hole diameter (&#34;Hole Dia.&#34;), step (&#34;Step&#34;), and range (&#34;Range&#34;) values for calculating a set of hole values. In general, the default setting works for most micro-CT bone samples, where noise, step, and range are 5, 5, 2, respectively, and hole diameter, step, and range are 15, 5, 2, respectively. Keep the default settings for the example dataset. 
	Note: &#34;Noise Dia.&#34; is the diameter for the noise suppression filter, and the &#34;Hole Dia.&#34; is the diameter for holes within the cortical bones. The noise and hole values used for parameter profiling can be calculated similarly as cortical threshold values described above, using the specified noise/hole, range, and step values. Due to the variations in scanning parameters when acquiring the images, noise and hole parameters should be determined empirically according to the image quality, as no general ranges of noise and hole parameters are good for all scans. The lowest value for threshold, noise, or holes must be greater than or equal to 0, and if the calculated lowest value is negative using the supplied parameters, the specific lowest value is set to 0.</li>
	<li>Click OK to perform parameter profiling. Visually check the segmentation results in the Parameter Profiling Results window, and select a slice layer so that the bone outer boundary is outlined quite accurately (Figure 1B). Subsequently, retrieve the profiling parameters from the entry in the Parameter Profiling Results table corresponding to the chosen slice layer (Table 1).</li>
</ol>
4. Trabecular Analysis
<ol>
	<li>Segmentation of trabecular bones
	<ol>
		<li>Open ImageJ software, then open or import a scanned image.</li>
		<li>Click the Plugins | BoMomics | Trab Segmentation button, and fill in appropriate analysis parameters. Set &#34;Start&#34;, &#34;Outline Boundary&#34;, &#34;Trab. Bones&#34;, &#34;Noise Reduction Dia.&#34;, &#34;Hole Filling Dia.&#34;, and &#34;Cortical Thickness Dia.&#34; to 5, 7,200, 7,000, 6, 12 and 25, respectively. 
		NOTE: &#34;Start&#34; and &#34;End&#34; specify the selected slice range for segmentation of trabecular bones, &#34;Outline Boundary&#34; is corresponding to the profiled &#34;Cort Bone&#34; parameter, &#34;Noise Reduction Dia.&#34; to &#34;Noise Dia.&#34; Parameter, and &#34;Hole Filling Dia.&#34; to &#34;Hole Dia.&#34; parameter. &#34;Cortical Thickness Dia.&#34; is the specified thickness for excluding the outer cortical bones. &#34;Trab. Bones&#34; is the threshold for extracting trabecular bones (Figure 2), where the settings are determined using profiling analysis parameters command as described in step 3.5.</li>
		<li>Click OK to perform trabecular segmentation. Visually check the segmentation results in the Trab Segmentation Results window (Figure 2B). Save the extracted trabecular bones shown in the Segmented Trabecular Bones window in a tiff format (Figure 2B), which can be further analyzed by other software.</li>
	</ol>
	</li>
	<li>Analysis of trabecular bones.
	<ol>
		<li>Open ImageJ software, then open or import a scanned image.</li>
		<li>Click the Plugins | BoMomics | Trab Analysis button, and fill in appropriate analysis parameters, such as &#34;Start&#34;, &#34;Outline Boundary&#34;, &#34;Trab. Bones&#34;, &#34;Noise Reduction Dia.&#34;, &#34;Hole Filling Dia.&#34;, and &#34;Cortical Thickness Dia.&#34; as described above (Figure 3A), where the settings are determined using the profiling analysis parameters command as described in step 3.5. Set &#34;Start&#34;, &#34;Outline Boundary&#34;, &#34;Trab. Bones&#34;, &#34;Noise Reduction Dia.&#34;, &#34;Hole Filling Dia.&#34;, and &#34;Cortical Thickness Dia.&#34; to 5, 7,200, 7,000, 6, 12 and 25, respectively. 
		NOTE: In the free version of the plugin, five adjacent slices starting from the specified &#34;Start&#34; slice index are selected for measurement, while in the commercial version, an arbitrary number of slices can be specified by the user.</li>
		<li>Select one or more choices in Results Reporting section for the parameters to be measured, where the trabecular bone volume (BV), total volume (TV) of the selected region, and thicknesses measured either two-dimensionally (2D) or three-dimensionally (3D) are available for selection through three checkboxes, namely &#34;BV TV Only&#34;, &#34;2D&#34;, and &#34;3D&#34;. Select the checkboxes &#34;2D&#34; and &#34;3D&#34;, then click &#34;OK&#34; to perform trabecular analysis&#160;(Figure 3A, Table 2). 
		NOTE: When &#34;BV TV Only&#34; is checked, regardless of the selection statuses of &#34;2D&#34; and &#34;3D&#34;, raw measures of BV, TV, and Intensity are reported, and the segmented trabecular bones are extracted and displayed in a new window, which can be saved and further analyzed by other software. When &#34;2D&#34; is checked, raw measures of BV, TV, Intensity, and thicknesses measured two-dimensionally at each slice level using the plate model are reported. If &#34;3D&#34; is checked, three-dimensional thickness for each voxel is calculated directly without any model assumption, then raw measures of BV, TV, Intensity, and three-dimensional thicknesses sampled at each slice level are reported. However, if no checkbox is selected, trabecular bones are segmented using the above set of parameters, but no measurement is reported.</li>
	</ol>
	</li>
</ol>
5. Quantifying Simulated Objects
<ol>
	<li>Open ImageJ software, then open a simulated image. Here, open the simulated sphere with a diameter of 30 pixels as an example.</li>
	<li>Select the Plugins | BoMomics | Trab Analysis button and fill in appropriate analysis parameters as described previously. Keep the default values for &#34;Start&#34;, &#34;End&#34;, &#34;Outline Boundary&#34;, &#34;Trab. Bones&#34;, and set &#34;Noise Reduction Dia.&#34;, &#34;Noise Reduction Dia.&#34;, &#34;Hole Filling Dia.&#34;, and &#34;Cortical Thickness Dia.&#34; to 0 (Figure 3). 
	Note: For simulated objects, there is no unintended signal noise, and there may be no corresponding cortical shells. Therefore, parameters for such values should be set appropriately (default settings are to zero). For 3D quantifications, 30 slices before and after the specified &#34;Start&#34; slice are processed using the free version of the plugin, while only slices in the range specified by &#34;Start&#34; and &#34;End&#34; slices are analyzed by the commercial version of the plugin.</li>
	<li>In the Results Reporting section, select &#34;2D&#34; and &#34;3D&#34; for the parameters to be measured, and click OK to perform trabecular analysis for the simulated object (Table 3).</li>
</ol>
6. Calibration of Trabecular Measures and Data Presentation: Profile the Distributions of Trabecular Measures in the Selected Analyzing Region
<ol>
	<li>Get the calibration information from the scanned dataset, according to the micro-CT vendor&#39;s instructions. 
	NOTE: Only raw measures of BV, TV, Intensity, and thicknesses are reported by the plugin. To get measures comparable to reports generated by other software, result calibration is desired.</li>
	<li>Open Microsoft Excel, and open the reported results table. Calculate calibrated bone volume (BV), total volume (TV), bone mineral content (BMC), bone volume fraction (BV/TV), and bone mineral density (BMD) in new excel columns using the reported raw BV, TV, and Intensity values according to the following expressions.</li>
	<li>NOTE: Scanning resolution (resolution, &#181;m), gray scaling (scaling), density units (mg HA/cm3), density slope (slope), and density intercept (intercept) can be extracted from the scanned micro-CT image or meta files, such as the Scanco ISQ file. Therefore, the calibrated measures are calculated as follows: 
	Suppose BV, TV and Intensity are raw measures, BVc and TVc are calibrated values, resolution, scaling, slope, intercept are from calibration files of micro - CT images. 
	BVc = BV &#215; resolution3 [&#956;m3] 
	TVc = TV &#215; resolution3 [&#956;m3] 
	BV / TV = BV &#247; TV 
	BMC = (Intensity &#247; scaling &#215; slope - BV&#160;&#215; intercept) &#215; resolution3 &#215; 10-12 [mg HA] 
	BMD -&#160;BMC &#247; TVc &#215; 1012 [mg HA / cm3]</li>
	<li>Create an XY (Scatter) plot for calibrated measures (Y) against slice layers (X) using Microsoft Excel software (Figure 4).</li>
</ol>

<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>

A patent for the underlying algorithm of quantifying object thickness has been filed. The authors collaborated with others registered the bomomics.com website hosting the free version plugins, where consulting and quantifying services of image analysis are provided upon request.

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><tbody><tr><td>ImageJ</td><td>NIH</td><td>imagej</td><td>Any version with a java 1.8 run time</td></tr><tr><td>trabecular analysis plugin</td><td>Bomomics</td><td>bomomics</td><td>free or commercial version</td></tr><tr><td>Micro CT scanner</td><td>Scanco</td><td>&amp;mu;-50</td><td>micro CT from any vendor</td></tr><tr><td>Computer System</td><td>Lenovo</td><td></td><td>any brand</td></tr><tr><td>Windows Operating System</td><td>Microsoft</td><td>Windows 7 x64</td><td>any 64-bit Windows operating system&amp;nbsp;</td></tr><tr><td>Office Software</td><td>Microsoft</td><td>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.
<p class="jove_content" fo...

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 ...

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

Research

JoVE Journal

Bioengineering

9.3K Views.  Wuhan University. We presenteren een workflow voor segmenteren en kwantificeren van trabecular beenderen voor 2D- en 3D-afbeeldingen op basis van de buitenste rand van het bot met behulp van een plugin ImageJ. Deze aanpak is efficiënter en nauwkeuriger dan de huidige handmatige hand-contouring aanpak, en laag-voor-laag kwantificeringen, die niet beschikbaar zijn in de huidige commerciële software biedt.

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

Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. However, the analysis and quantification of cellular localization is difficult, as in many cases it relies on manual counting, thus bearing the risk of introducing a rater-dependent bias and reducing interrater reliability. Moreover, it is often difficult to judge whether the co-localization between two cells results from random positioning, especially when cell types differ strongly in the frequency of their occurrence. Here, a method for unbiased quantification of cellular co-localization in the bone marrow is introduced. The protocol describes the sample preparation used to obtain histological sections of whole murine long bones including the bone marrow, as well as the staining protocol and the acquisition of high-resolution images. An analysis workflow spanning from the recognition of hematopoietic and non-hematopoietic cell types in 2-dimensional (2D) bone marrow images to the quantification of the direct contacts between those cells is presented. This also includes a neighborhood analysis, to obtain information about the cellular microenvironment surrounding a certain cell type. In order to evaluate whether co-localization of two cell types is the mere result of random cell positioning or reflects preferential associations between the cells, a simulation tool which is suitable for testing this hypothesis in the case of hematopoietic as well as stromal cells, is used. This approach is not limited to the bone marrow, and can be extended to other tissues to permit reproducible, quantitative analysis of histological data.

A strategy to quantitatively analyze histological data in the bone marrow is presented. Confocal microscopy of fluorescently labeled cells in tissue sections results in 2-dimensional images, which are automatically analyzed. Co-localization analyses of different cell types are compared to data from simulated images, giving quantitative information about cellular interactions.

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. ...

Developmental Biology

Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles

In cardiac myocytes a complex network of membrane tubules - the transverse-axial tubule system (TATS) - controls deep intracellular signaling functions. While the outer surface membrane and associated TATS membrane components appear to be continuous, there are substantial differences in lipid and protein content. In ventricular myocytes (VMs), certain TATS components are highly abundant contributing to rectilinear tubule networks and regular branching 3D architectures. It is thought that peripheral TATS components propagate action potentials from the cell surface to thousands of remote intracellular sarcoendoplasmic reticulum (SER) membrane contact domains, thereby activating intracellular Ca2+ release units (CRUs). In contrast to VMs, the organization and functional role of TATS membranes in atrial myocytes (AMs) is significantly different and much less understood. Taken together, quantitative structural characterization of TATS membrane networks in healthy and diseased myocytes is an essential prerequisite towards better understanding of functional plasticity and pathophysiological reorganization. Here, we present a strategic combination of protocols for direct quantitative analysis of TATS membrane networks in living VMs and AMs. For this, we accompany primary cell isolations of mouse VMs and/or AMs with critical quality control steps and direct membrane staining protocols for fluorescence imaging of TATS membranes. Using an optimized workflow for confocal or superresolution TATS image processing, binarized and skeletonized data are generated for quantitative analysis of the TATS network and its components. Unlike previously published indirect regional aggregate image analysis strategies, our protocols enable direct characterization of specific components and derive complex physiological properties of TATS membrane networks in living myocytes with high throughput and open access software tools. In summary, the combined protocol strategy can be readily applied for quantitative TATS network studies during physiological myocyte adaptation or disease changes, comparison of different cardiac or skeletal muscle cell types, phenotyping of transgenic models, and pharmacological or therapeutic interventions.

In cardiac myocytes, tubular membrane structures form intracellular networks. We describe optimized protocols for i) isolation of myocytes from mouse heart including quality control, ii) live cell staining for state-of-the-art fluorescence microscopy, and iii) direct image analysis to quantify the component complexity and the plasticity of intracellular membrane networks.

In cardiac myocytes a complex network of membrane tubules - the transverse-axial tubule system (TATS) - controls deep intracellular signaling functions. ...

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

The ability to visualize complex and dynamic physiological interactions between numerous cell types and the extracellular matrix (ECM) within a live tumor microenvironment is an important step toward understanding mechanisms that regulate tumor progression. While this can be accomplished through current intravital imaging techniques, it remains challenging due to the heterogeneous nature of tissues and the need for spatial context within the experimental observation. To this end, we have developed an intravital imaging workflow that pairs collagen second harmonic generation imaging, endogenous fluorescence from the metabolic co-factor NAD(P)H, and fluorescence lifetime imaging microscopy (FLIM) as a means to non-invasively compartmentalize the tumor microenvironment into basic domains of the tumor nest, the surrounding stroma or ECM, and the vasculature. This non-invasive protocol details the step-by-step process ranging from the acquisition of time-lapse images of mammary tumor models to post-processing analysis and image segmentation. The primary advantage of this workflow is that it exploits metabolic signatures to contextualize the dynamically changing live tumor microenvironment without the use of exogenous fluorescent labels, making it advantageous for human patient-derived xenograft (PDX) models and future clinical use where extrinsic fluorophores are not readily applicable.

The intravital imaging method described here utilizes collagen second harmonic generation and endogenous fluorescence from the metabolic co-factor NAD(P)H to non-invasively segment an unlabeled tumor microenvironment into tumor, stromal, and vascular compartments for in-depth analysis of 4D intravital images.

The ability to visualize complex and dynamic physiological interactions between numerous cell types and the extracellular matrix (ECM) within a live tumor ...

Cancer Research

Adult Mouse Digit Amputation and Regeneration: A Simple Model to Investigate Mammalian Blastema Formation and Intramembranous Ossification

Here, we present a protocol of adult mouse distal terminal phalanx (P3) amputation, a procedurally simple and reproducible mammalian model of epimorphic regeneration, which involves blastema formation and intramembranous ossification analyzed by fluorescence immunohistochemistry and sequential in-vivo microcomputed tomography (&#956;CT). Mammalian regeneration is restricted to amputations transecting the distal region of the terminal phalanx (P3); digits amputated at more proximal levels fail to regenerate and undergo fibrotic healing and scar formation. The regeneration response is mediated by the formation of a proliferative blastema, followed by bone regeneration via intramembranous ossification to restore the amputated skeletal length. P3 amputation is a preclinical model to investigate epimorphic regeneration in mammals, and is a powerful tool for the design of therapeutic strategies to replace fibrotic healing with a successful regenerative response. Our protocol uses fluorescence immunohistochemistry to 1) identify early-and-late blastema cell populations, 2) study revascularization in the context of regeneration, and 3) investigate intramembranous ossification without the need for complex bone stabilization devices. We also demonstrate the use of sequential in vivo &#956;CT to create high resolution images to examine morphological changes after amputation, as well as quantify volume and length changes in the same digit over the course of regeneration. We believe this protocol offers tremendous utility to investigate both epimorphic and tissue regenerative responses in mammals.

Here, we present a protocol of adult mouse terminal phalanx amputation to investigate mammalian blastema formation and intramembranous ossification, analyzed by fluorescent immunohistochemistry and sequential in-vivo microcomputed tomography.

Here, we present a protocol of adult mouse distal terminal phalanx (P3) amputation, a procedurally simple and reproducible mammalian model of epimorphic ...

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Osteoporosis-related vertebral compression fractures (OVCFs) are a common and clinically unmet need with increasing prevalence as the world population ages. Animal OVCF models are essential to the preclinical development of translational tissue engineering strategies. While a number of models currently exist, this protocol describes an optimized method for inducing multiple highly reproducible vertebral defects in a single nude rat. A novel longitudinal semiautomated microcomputed tomography (&#181;CT)-based quantitative structural analysis of the vertebral defects is also detailed. Briefly, rats were imaged at multiple time points post-op. The day 1 scan was reoriented to a standard position, and a standard volume of interest was defined. Subsequent &#181;CT scans of each rat were automatically registered to the day 1 scan so the same volume of interest was then analyzed to assess for new bone formation. This versatile approach can be adapted to a variety of other models where longitudinal imaging-based analysis could benefit from precise 3D semiautomated alignment. Taken together, this protocol describes a readily quantifiable and easily reproducible system for osteoporosis and bone research. The suggested protocol takes 4 months to induce osteoporosis in nude ovariectomized rats and between 2.7 and 4 h to generate, image, and analyze two vertebral defects, depending on tissue size and equipment.

The goal of this protocol is to generate a nude rat osteoporosis-related vertebral compression fracture model that can be longitudinally evaluated in vivo using a semiautomated microcomputed tomography-based quantitative structural analysis.

Osteoporosis-related vertebral compression fractures (OVCFs) are a common and clinically unmet need with increasing prevalence as the world population ...

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

In cardiac muscle, intracellular Ca2+ transients activate contractile myofilaments, causing contraction, macroscopic shortening, and geometric&#160;deformation. Our understanding of the internal relationships between these events has been limited because we can neither &#39;see&#39; inside the muscle nor precisely track the spatio-temporal nature of excitation-contraction dynamics. To resolve these problems, we have constructed a device that combines a suite of imaging modalities. Specifically, it integrates a brightfield microscope to measure local changes of sarcomere length and tissue strain, a fluorescence microscope to visualize the Ca2+ transient, and an optical coherence tomograph to capture the tissue&#39;s geometric&#160;changes throughout the time-course of a cardiac cycle. We present here the imaging infrastructure and associated data collection framework. Data are collected from isolated rod-like tissue structures known as trabeculae carneae. In our instrument, a pair of position-controlled platinum hooks hold each end of an ex vivo muscle sample while it is continuously superfused with nutrient-rich saline solution. The hooks are under independent control, permitting real-time control of muscle length and force. Lengthwise translation enables the piecewise scanning of the sample, overcoming limitations associated with the relative size of the microscope imaging window (540 &#181;m by 540 &#181;m) and the length of a typical trabecula (&#62;2000 &#181;m). Platinum electrodes at either end of the muscle chamber stimulate the trabecula at a user-defined rate. We exploit the stimulation signal as a trigger for synchronizing the data from each imaging window to reconstruct the entire sample twitching under steady-state conditions. Applying image-processing techniques to these brightfield imaging data provides tissue displacement and sarcomere length maps. Such a collection of data, when incorporated into an experiment-modeling pipeline, will provide a deeper understanding of muscle contractile homogeneity and heterogeneity in physiology and pathophysiology.

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

This protocol presents a collection of sarcomere, calcium, and macroscopic geometric&#160;data from an actively contracting cardiac trabecula ex vivo. These simultaneous measurements are made possible by the integration of three imaging modalities.

In cardiac muscle, intracellular Ca2+ transients activate contractile myofilaments, causing contraction, macroscopic shortening, and ...

TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis

Research in neuroscience has evolved to use complex imaging and computational tools to extract comprehensive information from data sets. Calcium imaging is a widely used technique that requires sophisticated software to obtain reliable results, but many laboratories struggle to adopt computational methods when updating protocols to meet modern standards. Difficulties arise due to a lack of programming knowledge and paywalls for software. In addition, cells of interest display movements in all directions during calcium imaging. Many approaches have been developed to correct the motion in the lateral (x/y) direction.
This paper describes a workflow using a new ImageJ plugin, TrackMate Analysis of Calcium Imaging (TACI), to examine motion on the z-axis in 3D calcium imaging. This software identifies the maximum fluorescence value from all the z-positions a neuron appears in and uses it to represent the neuron&#39;s intensity at the corresponding t-position. Therefore, this tool can separate neurons overlapping in the lateral (x/y) direction but appearing&#160;on distinct z-planes. As an ImageJ plugin, TACI is a user-friendly, open-source computational tool for 3D calcium imaging analysis. We validated this workflow using fly larval thermosensitive neurons that displayed movements in all directions during temperature fluctuation and a 3D calcium imaging dataset acquired from the fly brain.

TrackMate Analysis of Calcium Imaging (TACI) is an open-source ImageJ plugin for 3D calcium imaging analysis that examines motion on the z-axis and identifies the maximum value of each z-stack to represent a cell's intensity at the corresponding time point. It can separate neurons overlapping in the lateral (x/y) direction but on different z-planes.

Research in neuroscience has evolved to use complex imaging and computational tools to extract comprehensive information from data sets. Calcium imaging ...

Neuroscience

Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness

Subchondral bone thickening and sclerosis are the major hallmarks of osteoarthritis (OA), both in animal models and in humans. Currently, the severity of the histologic subchondral bone thickening is mostly determined by visual estimation based semi-quantitative grading systems. This article presents a reproducible and easily executed protocol to quantitatively measure subchondral bone thickness in a mouse model of knee OA induced by destabilization of the medial meniscus (DMM). This protocol utilized ImageJ software to quantify subchondral bone thickness on histologic images after defining a region of interest in the medial femoral condyle and the medical tibial plateau where subchondral bone thickening usually occurs in DMM-induced knee OA. Histologic images from knee joints with a sham procedure were used as controls. Statistical analysis indicated that the newly developed quantitative subchondral bone measurement system was highly reproducible with low intra- and inter-observer variabilities. The results suggest that the new protocol is more sensitive to subtle or mild subchondral bone thickening than the widely used visual grading systems. This protocol is suitable for detecting both early and progressing osteoarthritic subchondral bone changes and for assessing in vivo efficacy of OA treatments in concert with OA cartilage grading.

This methodology article presents a software-assisted quantitative measurement protocol to quantify histologic subchondral bone thickness in murine osteoarthritic knee joints and normal knee joints as controls. This protocol is highly sensitive to subtle thickening and is suitable for detecting early osteoarthritic subchondral bone changes.

Subchondral bone thickening and sclerosis are the major hallmarks of osteoarthritis (OA), both in animal models and in humans. Currently, the severity of ...

Medicine

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

The National Institute of Health&#39;s ImageJ is a powerful, freely available image processing software suite. ImageJ has comprehensive particle analysis algorithms which can be used effectively to count various biological particles. When counting large numbers of cell samples, the hemocytometer presents a bottleneck with regards to time. Likewise, counting membranes from migration/invasion assays with the ImageJ plugin Cell Counter, although accurate, is exceptionally labor intensive, subjective, and infamous for causing wrist pain. To address this need, we developed two plugins within ImageJ for the sole task of automated hemocytometer (or known volume) and migration/invasion cell counting. Both plugins rely on the ability to acquire high quality micrographs with minimal background. They are easy to use and optimized for quick counting and analysis of large sample sizes with built-in analysis tools to help calibration of counts. By combining the core principles of Cell Counter with an automated counting algorithm and post-counting analysis, this greatly increases the ease with which migration assays can be processed without any loss of accuracy.

This paper describes the quantification of hemocytometer and migration/invasion micrographs through two new open-source ImageJ plugins Cell Concentration Calculator and migration assay Counter. Furthermore, it describes image acquisition and calibration protocols as well as discusses in detail the input requirements of the plugins.

The National Institute of Health&#39;s ImageJ is a powerful, freely available image processing software suite. ImageJ has comprehensive particle analysis ...

Biology

HE Staining and Micro-CT to Evaluate Murine Trabecular Bone Microarchitecture

Trabecular Bone Microarchitecture Evaluation in an Osteoporosis Mouse Model

Bone microstructure refers to the arrangement and quality of bone tissue at the microscopic level. Understanding the bone microstructure of the skeleton is crucial for gaining insight into the pathophysiology of osteoporosis and improving its treatment. However, handling bone samples can be complex due to their hard and dense properties. Secondly, specialized software makes image processing and analysis difficult. In this protocol, we present a cost-effective and easy-to-use solution for trabecular bone microstructure analysis. Detailed steps and precautions are provided. Micro-CT is a non-destructive three-dimensional (3D) imaging technique that provides high-resolution images of trabecular bone structure. It allows for the objective and quantitative evaluation of bone quality, which is why it is widely regarded as the gold standard method for bone quality assessment. However, histomorphometry remains indispensable as it offers crucial cellular-level parameters, bridging the gap between two-dimensional (2D) and 3D assessments of bone specimens. As for the histologic techniques, we chose to decalcify the bone tissue and then perform traditional paraffin embedding. In summary, combining these two methods can provide more comprehensive and accurate information on bone microstructure.

Author Spotlight: An Economic and Efficient Method for Quantitative Evaluation of Bone Microarchitecture in a Murine Osteoporosis Model 

This protocol presents an economical and efficient method for quantitative evaluation of bone microarchitecture in a mouse model of osteoporosis by combining Hematoxylin-Eosin (HE) staining and micro-computed tomography (Micro-CT) techniques.

Bone microstructure refers to the arrangement and quality of bone tissue at the microscopic level. Understanding the bone microstructure of the skeleton ...

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

Samenvatting

Meer video's verkennen

Hoofdstukken in deze video