这项工作得到了赠款 NFSC 81170806 的部分支持。作者感谢武汉大学口腔医学院的微 CT 核心设施, 帮助对大鼠股骨进行扫描和分析。

涉及动物主题的程序是按照《动物保育和使用指南》 (NIH 出版, 第八版, 2011) 进行的, 并已由武汉机构动物保育和使用委员会审查和批准。大学.
1. 软件安装
<ol><li>安装 ImageJ 软件。从 https://imagej.nih.gov/ij/下载与64位 Java 捆绑在一起的 ImageJ (版本 1.51p) 软件的 Windows 版本。将下载的软件解压到一个文件夹中, 随后将其称为 "ImageJ 目录"。 注: 小梁分析插件需要64位 java 运行时间 (版本 1.8) 和64位 windows 操作系统, 最好是64位 windows 7 操作系统。</li>
	<li>安装小梁分析插件。请求小梁分析插件从 http://www.bomomics.com, 并解压的内容到 ImageJ 的插件目录, 这是 "ImageJ 目录/插件"。 注: 该插件可以作为一个自由版本获得, 其中5指定相邻切片的测量报告, 或作为商业版本, 其中的切片范围可以指定和测量。</li>
</ol>2. 为小梁分析准备3D 数据集
<ol><li>在标准扫描协议5之后, 用微型 CT 扫描大鼠股骨, 然后以可导入 ImageJ 的格式 (例如) tiff 格式保存数据。如果在单个样本管中同时扫描多个骨样本, 首先将数据导入 ImageJ, 然后使用 ImageJ 的图像处理工具裁剪其他样本, 从而分离出每个骨骼。随后, 将生成的图像保存为可在以后导入 ImageJ 的格式。 注意: 分析中使用的代表性示例图像文件 (辅助文件 1)。</li>
	<li>
模拟标准2D 和3D 对象。
	<ol>
<li>打开 ImageJ 软件。在插件 |BoMomics |模拟对象菜单, 单击圆圈按钮, 然后在弹出窗口中输入200作为直径, 然后单击OK以生成直径为200像素的模拟圆 (图 3B)。以 tiff 格式保存生成的圆。</li>
		<li>打开 ImageJ 软件。在插件 |BoMomics |模拟对象菜单, 单击正方形按钮, 然后在弹出窗口中输入200作为侧面长度, 然后单击OK以生成具有200像素的边长 (图 3B) 的模拟正方形。以 tiff 格式保存生成的正方形。</li>
		<li>打开 ImageJ 软件。在插件 |BoMomics |模拟对象菜单, 单击矩形按钮, 然后在弹出窗口中输入200作为宽度和100作为高度, 然后单击确定生成具有200像素宽度和100像素高度的模拟矩形 (图3 B)。以 tiff 格式保存生成的矩形。 注: 一个圆 (直径: 200 像素), 正方形 (边长: 200 像素) 和一个矩形 (宽度: 200 像素; 高度: 100 像素) 为后续分析保存。</li>
		<li>打开 ImageJ 软件。在插件 |BoMomics |模拟对象菜单, 单击多维数据集按钮, 然后在弹出窗口中输入30作为侧面长度, 然后单击确定生成模拟多维数据集。最后, 单击插件 | 3D |卷查看器查看生成的多维数据集并以 tiff 格式保存它 (图 3C)。</li>
		<li>打开 ImageJ 软件。在插件 |BoMomics |模拟对象菜单, 单击长方体按钮, 然后输入80作为长度, 40 作为宽度, 30 作为高度在弹出窗口中, 然后单击确定生成模拟长方体。最后, 单击插件 | 3D |卷查看器查看生成的长方体并以 tiff 格式保存它 (图 3C)。</li>
		<li>打开 ImageJ 软件。在插件 |BoMomics |模拟对象菜单, 单击球面按钮, 然后在弹出窗口中输入30作为直径, 然后单击确定生成模拟球体。最后, 单击插件 | 3D |卷查看器查看生成的球体并以 tiff 格式保存它 (图 3C)。</li>
		<li>打开 ImageJ 软件。在插件 |BoMomics |模拟对象菜单, 单击钢瓶按钮, 然后在弹出窗口中输入30作为直径和100作为高度, 然后单击确定生成模拟气缸。最后, 单击插件 | 3D |卷查看器查看生成的气缸并以 tiff 格式保存它 (图 3C)。 注: 球体 (直径:30 像素), 立方体 (边长:30 像素), 长方体 (长度:80 像素; 宽度:40 像素; 高度:30 像素) 和圆筒 (直径:30 像素; 高度: 100 像素) 为后续分析保存。</li>
	</ol>
</li>
</ol>3. 性能分析参数
<ol><li>打开 ImageJ 软件, 打开或导入扫描的图像。</li>
	<li>滑动底部滚动条以选择一个切片, 然后单击图像 |调整 |阈值按钮。在 "弹出式阈值" 窗口中, 通过手动检查调整最小和最大阈值, 以确保骨骼与背景分离良好, 并将最小阈值记录为皮质骨阈值。 注: 由于不同的微 CT 制造商使用不同的比例因子来存储图像中的 X 射线衰减系数, 所以实际的阈值应该通过手动检查或按照特定制造商的建议.在我们的实践中, 设置最小阈值的 ISQ 文件生产的微型 CT 机到 6,000-7, 000 可以可靠地分离骨骼的扫描背景。</li>
	<li>单击插件 |BoMomics |Trab 参数分析按钮。在弹出窗口中, 将切片索引设置为代表切片的位置, 并设置皮质骨 ("Cort 骨")、范围 ("范围") 和步骤 ("步长") 值, 用于计算用于分析分割参数的一组皮质门限, 其中皮质骨阈值由步骤 3.2 (图 1) 获取。默认范围和步骤值为2000和400为大多数微 CT 图像工作。将切片索引设置为示例数据集的 5, 并保留其他设置的默认值。 注: "Cort 骨" 是皮质骨的门槛, 用于参数分析的阈值将从最低阈值更改为最高阈值, 并增加步长值, 其中最低阈值是 Cort 的值。骨范围和最高阈值是 Cort 骨 + 范围。如果 Cort 骨骼、范围和步骤的 set 值分别为6000、1000和 500, 则皮质骨骼的最低阈值为6,000-1、000 = 5000, 最高阈值为 6000 + 1000 = 7000, 分析中使用的阈限为 5000,5500、6000、6500和7000。</li>
	<li>设置噪音直径 ("噪音直径"), 步骤 ("步骤"), 和范围 ("范围") 值, 用于指定分析中的一组噪声值, 以及用于计算一组孔值的孔直径 ("孔直径")、步骤 ("步长") 和范围 ("范围") 值。通常, 默认设置适用于大多数微 CT 骨样, 其中噪声、步骤和范围分别为5、5、2、孔径、步长和范围分别为15、5、2。保留示例数据集的默认设置。 注: "噪音直径" 是噪音抑制过滤器的直径, 而 "孔直径" 是皮质骨内孔的直径。用于参数分析的噪声和孔值可以类似于上面描述的皮质阈值, 使用指定的噪声/孔、范围和步长值。由于在采集图像时扫描参数的变化, 噪声和孔参数应根据图像质量进行实证确定, 因为没有一般的噪声范围和孔参数对所有扫描都有好处。阈值、噪音或孔的最低数值必须大于或等于 0, 并且如果使用提供的参数计算出的最低值为负值, 则将特定的最低值设置为0。</li>
	<li>单击确定以执行参数分析。在 "参数分析结果" 窗口中直观地检查分割结果, 然后选择切片图层, 以便准确地勾勒出骨骼外部边界 (图 1B)。随后, 从参数分析结果表中与所选切片层对应的项中检索分析参数 (表 1)。</li>
</ol>4. 小梁分析
<ol><li>
小梁骨的分割
	<ol>
<li>打开 ImageJ 软件, 然后打开或导入扫描的图像。</li>
		<li>单击插件 |BoMomics |Trab 细分按钮, 并填写适当的分析参数。设置 "开始", "轮廓边界", "Trab"。骨骼 "," 降噪直径 "," 孔填充直径 ", 和" 皮质厚度直径 "分别为5、7200、7000、6、12和25。 注: "开始" 和 "结束" 指定选定切片范围的小梁骨分割, "轮廓边界" 是对应的异形 "Cort 骨" 参数, "降噪直径" 到 "噪音直径"。参数, 并 "孔填充直径" 到 "孔直径" 参数。"皮质厚度直径" 是排除外皮质骨的指定厚度。trab.骨骼 "是提取小梁骨的门槛 (图 2), 其中设置是使用分析分析参数命令确定的, 如步骤3.5 中所述。</li>
		<li>单击确定执行小梁分割。在Trab 细分结果窗口 (图 2B) 中直观地检查分割结果。以 tiff 格式 (图 2B) 中的分段小梁骨窗口保存所提取的小梁骨, 可由其他软件进一步分析。</li>
	</ol>
</li>
	<li>
小梁骨分析。
	<ol>
<li>打开 ImageJ 软件, 然后打开或导入扫描的图像。</li>
		<li>单击插件 |BoMomics |Trab 分析按钮, 并填写适当的分析参数, 如 "开始"、"轮廓边界"、"Trab"。骨骼 "," 降噪直径 "," 孔填充直径 ", 和" 皮质厚度直径 "如上所述 (图 3A), 其中设置是使用" 分析分析参数 "命令确定的, 如步骤3.5 中所述。设置 "开始", "轮廓边界", "Trab"。骨骼 "," 降噪直径 "," 孔填充直径 ", 和" 皮质厚度直径 "分别为5、7200、7000、6、12和25。 注意: 在插件的自由版本中, 五个相邻切片从指定的 "开始" 切片索引开始选择用于度量, 而在商业版本中, 用户可以指定任意数量的切片。</li>
		<li>在 "结果报告" 部分中, 为要测量的参数选择一个或多个选项, 其中所选区域的小梁骨量 (BV)、总容积 (TV) 和厚度均测量为二维 (2D) 或三维(3D) 可供选择, 通过三个复选框, 即 "BV 电视", "2D", 和 "3D"。选择复选框 "2D" 和 "3D", 然后单击 "确定" 执行小梁分析 (图 3A,表 2)。 注: 当检查 "bv 电视" 时, 无论 "2D" 和 "3D" 的选择状态如何, 都报告了 bv、电视和强度的原始测量方法, 并将分割后的小梁骨提取并显示在一个新窗口中, 可以保存并进一步分析其他软件.在检查 "2D" 时, 报告了在每个切片级别上用平板模型测量的 BV、电视、强度和厚度的原始测量值。如果检查 "3D", 则在没有任何模型假设的情况下直接计算每个体素的三维厚度, 然后报告每个切片级别取样的 BV、电视、强度和三维厚度的原始度量值。但是, 如果未选择复选框, 则使用上述参数集对小梁骨进行分段, 但不报告任何测量。</li>
	</ol>
</li>
</ol>5. 量化模拟物体
<ol><li>打开 ImageJ 软件, 然后打开一个模拟图像。在这里, 打开模拟球体的直径为30像素的例子。</li>
	<li>选择插件 |BoMomics |Trab 分析按钮并填写相应的分析参数, 如前所述。保留默认值为 "开始"、"结束"、"轮廓边界"、"Trab"。骨骼 ", 并设置" 降噪直径 "," 降噪直径 "," 孔填充直径 ", 和" 皮质厚度直径 "到 0 (图 3)。 注意:对于模拟对象, 没有意外的信号噪声, 也可能没有相应的皮层壳。因此, 应适当地设置这些值的参数 (默认设置为零)。对于 3D quantifications, 在指定的 "开始" 切片之前和之后使用插件的自由版本处理30切片, 而只有由 "开始" 和 "结束" 切片指定的区域中的切片才能通过插件的商业版本进行分析。</li>
	<li>在结果报告部分中, 为要测量的参数选择 "2D" 和 "3D", 然后单击确定对模拟对象执行小梁分析 (表 3)。</li>
</ol>6. 小梁测量和数据表示的校准: 剖面分析区域中小梁的分布
<ol><li>根据微 CT 供应商的说明, 从扫描数据集中获取校准信息。 注意: 只有原始测量的 BV, 电视, 强度和厚度是由插件报告。为了获得与其他软件生成的报告类似的度量值, 需要对结果进行校准。</li>
	<li>打开 "Microsoft Excel", 然后打开 "报告的结果" 表。根据以下表达式计算新 excel 列中校准的骨量 (bv)、总容积 (TV)、骨矿物质含量 (BMC)、骨量分数 (bv/TV) 和骨密度 (BMD)。</li>
	<li>注: 扫描分辨率 (分辨率、µm)、灰度缩放 (缩放)、密度单位 (毫克 HA/cm3)、密度斜率 (斜率) 和密度截距 (拦截) 可以从扫描的微 CT 图像或元文件 (如 Scanco ISQ 文件) 中提取。因此, 校准的措施计算如下: 假设 bv、电视和强度都是原始测量, BVc和电视c是校准值、分辨率、缩放、斜率、截距均来自于微 CT 图像的校准文件。 bvc = BV x 分辨率3 [微米3] 电视c = 电视 x 分辨率3 [微米3] bv/电视 = bv ÷电视 BMC = (强度÷缩放 x 斜率-BV x 拦截) x 分辨率3 x 10-12 [毫克 HA] 骨密度-BMC ÷电视c x 1012 [毫克 HA/cm3]</li>
	<li>使用 Microsoft Excel 软件 (图 4) 创建针对切片层 (X) 的校准度量值 (Y) 的 XY (散点) 图。</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>

提出了一种用于量化物体厚度的底层算法的专利。作者与其他人合作, 注册了 bomomics.com 网站, 托管免费版本插件, 在这里提供咨询和量化图像分析服务的要求。

本研究介绍了一种用于分析小梁骨的 ImageJ 插件, 它具有自动、高效和人性化的功效。该插件还可用于量化任何2D 或3D 对象, 用于逐层度量区域、卷和厚度。目前, 通过标准的显微 CT 分析, 每个样品的每一个小梁参数只有一个测量值, 不能完全代表所选分析区域中测量实体的特征。所述插件报告每个样本的每一个参数的逐层数量, 在选定的分析区域充分保留测量参数的分布信息, 因此更先进和敏感的统计方法适用于分析此类数据。
采用标准的半自动手工轮廓分割方法, 用算子偏置法观察了不同手形切片之间的显著差异8,9,10, 要求更统一和自动法进行小梁分割。由于扫描背景与骨外边界之间的对比度最大, 外边界辅助小梁的分割是由插件执行的, 在适当的时候, 骨骼的外边界可以自动检测出准确的提供阈值、噪音和孔设置。为便于对分割参数的确定, 在分析区域中用代表性切片对一系列参数组合进行了描述, 并对满意的分割结果进行了直观的检查。随后, 用异型参数对小梁骨进行分析。当设置合适的分割参数时, 该插件成功应用于不同骨同化剂治疗大鼠远端股骨小梁骨的分析5, 为同一图像报告可重现的结果没有操作员的偏见。
插件目前只能一次处理一个样本。如果图像中存在多个样本, 则所有样本都被量化为单个对象而不受歧视。因此, 如果在单个采样管中同时扫描多个样本, 则需要进行预处理。量化输出是像素计数或灰度值的原始度量, 应使用适当的校准信息手动校准。
该协议中最关键的一步是为小梁分析选择合适的参数。通常, 用相同的分割参数分析骨样本组, 使结果可比。在这种情况下, 应注意确保在进行进一步分析之前, 在视觉上检查所有样本的分割结果。
这项技术的一个主要限制是, 降噪设置适用于皮质骨仅用于轮廓的骨骼的外部边界, 但不是在提取的小梁骨。随着各种过滤策略的报告15在不同条件下的小梁分析是最佳的, 没有单一的过滤器对所有的图像样本都有好处。因此, 在定量小梁骨之前没有内置的过滤步骤。为了与内置图像过滤软件的输出兼容, 提取的小梁骨可以使用各种成像工具进行相似的处理, 然后导入 ImageJ, 然后由插件进行量化。另外, 不同的图像格式由不同的微 CT 供应商产生, 其中大部分不能直接由 ImageJ 输入或打开, 因此需要将各种图像格式导入 ImageJ 的模块进行进一步的分析。另一个限制是, 如果部分对象位于选择范围之外, 则在选择范围的末尾附近的切片中的3D 厚度是不准确的, 因此对于任何可用的3D 量化软件包也是如此, 因此, 更大范围的切片应量化, 只有选定切片的中间部分应用于进一步分析。这种方法只能在被测量的数量逐层报告的情况下才可用, 如本插件所示, 但不能用于测量, 仅报告选定范围内每个量化参数的单个值。

小梁骨显微 CT 分析是跟踪不同骨丢失情况下动物骨骼形态学变化的标准方法1,2,3, 其中几个变量与报告了骨骼的结构4。但是, 此类参数在长骨骼的骺中不均匀分布5, 并且仅报告每个样本的每个结构变量的汇总值或平均值 (由当前商用微 CT 机6,7, 但单个值不能完全表示分析区域中测量参数的特征。一层一层定量的小梁骨不仅保留了对每个变量的更多信息, 而且还能够在分析区域中对此类变量的分布进行分析, 便于随后对结构进行统计分析。在不同条件下的更改5。因此, 该方法的目的是量化微梁骨的显微 ct 扫描在每个切片水平, 这是没有在任何商业可用的微 ct 分析包目前。
为了有效地分割小梁骨切片, 需要自动分割方法。然而, 目前的显微 CT 分析标准技术是基于人工交互式轮廓, 然后是半自动插补, 将小梁骨从皮质隔间分离出来, 这是劳动密集型、易出错和与大量运算符偏差关联8,9,10。自动分割方法11,12报告, 但这种方法只有在良好的分离小梁骨和皮质骨的区域, 但不是在没有明确的分离区域。另外, 不同的样本12需要不同的分割参数, 通过尝试各种参数组合12, 手动选择适用于骨骼样本组的满意的分割参数是很繁琐的,即使在设置所有相关参数时, 分割过程也是自动的。由于骨外边界与扫描背景有最大的对比, 长骨骼的骺皮质壳在所选择的分析区域中表现出很少的变化, 根据长骨骼外边界轮廓分割方法可以可靠准确地将小梁骨从皮质壳中分离出来。这种分割方法的优点是, 分割是基于背景和骨骼的外边界之间的差异, 而不是小梁和皮质骨骼之间的差异6,12, 13, 因此通常很容易找到一个组合的分割参数, 这是令人满意的一组骨样本, 有助于更可靠地分析不同组之间的小梁变化。
在每个切片级别上, 报告了2D 分析的面积、周长和二维 (2D) 厚度, 而在 3D quantifications 中报告了体积、表面和三维 (3D) 厚度。目前的图像分析工具通常不报告此类信息, 表明所报告的程序可应用于需要此类信息的一般图像。

<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

微计算机断层扫描 (显微 CT) 通常用于评估不同骨质流失情况下小动物骨量和微梁的显微结构特征。然而, 显微 CT 图像小梁分析的标准方法是切片半自动手工轮廓, 这是劳动密集型和易出错的。本文描述的是根据骨的外边界自动分割小梁骨的有效方法, 在适当的情况下, 在合适的情况下, 可自动识别和分割短梁骨。设置了分割参数。为了分析满意的分割参数, 给出了分割结果的图像栈, 将分割参数的所有可能组合按顺序变化, 并通过相关参数的分割结果可以易于目视检查。作为插件的质量控制特性, 模拟标准对象被量化, 在测量量可以与理论值进行比较。该插件报告了小梁性质和小梁厚度的逐层量化, 并能很容易地对所选区域内的这种性质分布进行分析。虽然逐层量化保留了关于小梁骨的更多信息, 并有助于进一步统计分析结构变化, 但此类措施无法从当前商业软件的输出中获得, 只有单一每个示例都报告每个参数的量化值。因此, 所描述的工作流是比较准确、高效地分析小梁骨的较好方法。

小梁分析插件旨在自动分割和量化小梁骨的准确性。最初, 骨外边界被发现和划定后, 一个孔充填手术, 其中任何孔内的骨外皮质壳填补。然后进行侵蚀手术, 排除外皮质骨, 并获得分割小梁骨。最后, 对分段区小梁骨的测量方法进行量化。
由于微 CT 图像固有的噪音, 分割使用预定义的任意参数往往无法准确识别骨骼的外部边界。为了选择满意的分割参数, 尝试大量的参数组合, 是一种繁琐而又费力的工作。因此, 提供了一个参数分析插件, 用于在设置范围内自动更改参数, 以帮助选择满意的参数组合, 这也便于为一组骨骼选择一条共同的参数集。样品.图 1A显示用于分析好的分割参数的设置。如果指定了皮质骨阈值 (Cort 骨) 的参数、要分析的阈值范围 (范围) 以及每个步骤的阈值 (步长) 的增量量, 则会生成一系列要分析的阈值。随后, 通过设置相应的参数, 生成了一系列类似的噪声和孔值。最后, 通过改变所有可能的参数组合的参数一次, 对小梁骨进行分段。图 1B显示不同参数组合的代表性细分结果。显然, 一些参数组合在划定骨骼的外边界方面比其他的更好, 而多个参数组合显示出满意的分割结果。在目视检查分割结果后, 可以从分析结果表 (表 1) 中检索满意分割的参数值。
为量化小梁骨的测量方法, 进行小梁的分割和分析。图 2A 显示了小梁分割的设置对话框, 其中选定区域中的小梁骨被分割和提取 (图 2B), 使用提供的参数的分割结果可以按切片进行检查视觉.随后, 用满意的参数分析小梁骨 (图 3A)。根据报告选项的选择状态, 报告了骨量 (BV) 的原始 quantifications、总容积 (TV)、灰度值总和 (强度) 和测量两维或三尺寸 (表 2) 的厚度。最后, 从扫描的微 CT 数据集中提取校准信息, 并对 bv、电视、BMC、bv/电视等的校准措施进行了计算, 并对其分布情况进行了分析, 并对所选分析区域逐层对层进行了描述。位置 (图 4)。
作为插件的质量控制特性, 支持模拟对象的量化。模拟标准对象的已知维度由插件进行量化, 以便与其他软件的理论价值或措施进行比较, 例如提供微 CT 机或 BoneJ14的商用软件, 一个免费的开源插件。用于骨骼图像分析。为了分析模拟对象, 噪声设置设置为零, 因为模拟图像被认为是高质量的图像, 没有任何噪音。模拟了各种厚度的对象, 并显示了结果 (图 3,表 3)。对于模拟标准2D 对象 (如圆圈、正方形和矩形), 报告区域 (电视或 BV) 和厚度的精确值 (表 3)。对于3D 对象, 报告了多维数据集、球体和长方体的精确厚度度量值, 但是, 气缸的厚度对于素附近的两端是不准确的, 而气缸中间片中的体素厚度恰恰是预测.这是底层厚度测量算法的一个特点, 其中每个物体的体素厚度是由最大球体的直径或最大立方体的侧长来决定的, 其中包含这个体素, 完全在对象.因此, 可以精确地测量不同球体和立方体的物体厚度, 而气缸只能在中间片的半径距离内精确测量。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57178/57178fig1.jpg"> 图 1: 参数分析分析的代表性结果.(A) "参数设置" 页。(B) 参数分析分析的代表性结果。某些参数组合比其他方法更好地检测骨骼的外边界。<a href="//cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/57178/57178fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57178/57178fig2.jpg"> 图 2: 小梁分割分析的典型结果.(A) "参数设置" 页。(B) 有代表性的小梁骨在不同层的分割结果。<a href="//cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/57178/57178fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57178/57178fig3.jpg"> 图 3: 小梁分析.(A) "参数设置" 页。(B) 模拟2D 对象的代表性结果。(C) 由3D 卷查看器可视化的模拟3D 对象的典型结果。<a href="//cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/57178/57178fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57178/57178fig4.jpg"> 图 4: 在选定的分析区域中分配小梁骨量值.水平轴表示分析区域中起始切片层的相对距离。Y 轴中的值是在分析区域中校准小梁的措施。<a href="//cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/57178/57178fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<table fo:keep-together.within-page="1"><tbody>
<tr>
<td>切片</td>
			<td>轮廓边界</td>
			<td>降噪直径</td>
			<td>孔填充直径。</td>
		</tr>
<tr>
<td>4</td>
			<td>5200</td>
			<td>0</td>
			<td>10</td>
		</tr>
<tr>
<td>147</td>
			<td>5200</td>
			<td>2</td>
			<td>20</td>
		</tr>
<tr>
<td>361</td>
			<td>7200</td>
			<td>6</td>
			<td>16</td>
		</tr>
<tr>
<td>539</td>
			<td>8000</td>
			<td>10</td>
			<td>20</td>
		</tr>
</tbody></table>表 1: 参数分析分析的代表性结果。
<img alt="Table 2" src="/files/ftp_upload/57178/57178table2.jpg"> 表 2: 小梁分析的代表性结果。
<table fo:keep-together.within-page="1"><tbody>
<tr>
<td>对象</td>
			<td>尺寸</td>
			<td>卷b</td>
			<td>曲面c</td>
			<td>厚度</td>
		</tr>
<tr>
<td>广场</td>
			<td>200 X 200</td>
			<td>40000</td>
			<td>796</td>
			<td>200</td>
		</tr>
<tr>
<td>矩形</td>
			<td>200 X 100</td>
			<td>20000</td>
			<td>596</td>
			<td>100</td>
		</tr>
<tr>
<td>圈</td>
			<td>直径: 200</td>
			<td>31428</td>
			<td>796</td>
			<td>200</td>
		</tr>
<tr>
<td>立方体</td>
			<td>30 x 30 x 30</td>
			<td>27000</td>
			<td>5048</td>
			<td>30</td>
		</tr>
<tr>
<td>长方体</td>
			<td>80 x 40 x 30</td>
			<td>96000</td>
			<td>13008</td>
			<td>30</td>
		</tr>
<tr>
<td>领域</td>
			<td>直径:30</td>
			<td>14328</td>
			<td>3944</td>
			<td>30</td>
		</tr>
<tr>
<td>钢瓶d
</td>
			<td>Dia:30;H: 100</td>
			<td>71600</td>
			<td>12800</td>
			<td>27.84</td>
		</tr>
<tr>
<td>圆柱e
</td>
			<td>Dia:30;H: 100</td>
			<td>51552</td>
			<td>9552</td>
			<td>30</td>
		</tr>
<tr>
<td colspan="5">a: 结果是在原始素。外径: 直径;H: 高度。</td>
		</tr>
<tr>
<td colspan="5">b: 容积 (3D) 或面积 (2D)。</td>
		</tr>
<tr>
<td colspan="5">c: 表面 (3D) 或周长 (2D)。</td>
		</tr>
<tr>
<td colspan="5">d: 从切片1到切片100的措施用于分析。</td>
		</tr>
<tr>
<td colspan="5">e: 从切片15到切片85的措施用于分析。</td>
		</tr>
</tbody></table>表 3: 模拟对象的量化结果。
<img alt="Supplementary File 1" src="/files/ftp_upload/57178/57178supfig1.jpg"> 辅助文件1。样本骨.<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/57178/SampleBone.tif.crdownload" target="_blank">请单击此处下载此文件.</a>

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

我们提出了一个工作流, 以分割和量化的小梁骨2D 和3D 图像基于骨骼的外部边界使用一个 ImageJ 插件。这种方法比当前手工轮廓方法更有效和准确, 并提供了一层层的 quantifications, 在当前的商业软件中是不可用的。

用 Imagej 插件对小梁骨的外边界辅助分割和量化

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

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9.2K 次观看.  Wuhan University. 我们提出了一个工作流, 以分割和量化的小梁骨2D 和3D 图像基于骨骼的外部边界使用一个 ImageJ 插件。这种方法比当前手工轮廓方法更有效和准确, 并提供了一层层的 quantifications, 在当前的商业软件中是不可用的。

视频: 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.

造血干细胞的自动定量 - 在脱钙骨的组织学图像间质细胞的相互作用

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

科研

发育生物学

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.

管式膜网络在心肌细胞的心房和心室分析

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

生物工程

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.

一种用于乳腺肿瘤微环境活体内成像的无标记分割方法

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

癌症研究

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.

成年小鼠数字截肢和再生:研究哺乳动物暴发体形成和内膜性细胞化的简单模型

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.

半自动纵 Microcomputed Tomography-based 裸鼠骨质疏松症相关椎体骨折模型的定量结构分析

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.

同时布莱特菲尔德， 荧光， 和光学相干断层成像收缩心脏特拉贝库莱 Ex Vivo

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.

TACI:用于 3D 钙成像分析的 ImageJ 插件

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

神经科学

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.

软件辅助骨关节炎软骨下骨厚度的定量测量

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

医学

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.

自动定量分析细胞计数程序使用ImageJ插件

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

生物学

HE 染色和 micro-CT 评估小鼠骨小梁微结构

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.

作者聚焦:一种经济有效的小鼠骨质疏松症模型中骨微结构定量评估的方法 

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.

骨质疏松症小鼠模型中的骨小梁微观结构评估

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

用 Imagej 插件对小梁骨的外边界辅助分割和量化

摘要

探索更多视频

此视频中的章节

造血干细胞的自动定量 - 在脱钙骨的组织学图像间质细胞的相互作用

管式膜网络在心肌细胞的心房和心室分析

一种用于乳腺肿瘤微环境活体内成像的无标记分割方法

成年小鼠数字截肢和再生:研究哺乳动物暴发体形成和内膜性细胞化的简单模型

半自动纵 Microcomputed Tomography-based 裸鼠骨质疏松症相关椎体骨折模型的定量结构分析

同时布莱特菲尔德，荧光，和光学相干断层成像收缩心脏特拉贝库莱 Ex Vivo

TACI:用于 3D 钙成像分析的 ImageJ 插件

软件辅助骨关节炎软骨下骨厚度的定量测量

自动定量分析细胞计数程序使用ImageJ插件

骨质疏松症小鼠模型中的骨小梁微观结构评估

用 Imagej 插件对小梁骨的外边界辅助分割和量化

摘要

探索更多视频

此视频中的章节

造血干细胞的自动定量 - 在脱钙骨的组织学图像间质细胞的相互作用

管式膜网络在心肌细胞的心房和心室分析

一种用于乳腺肿瘤微环境活体内成像的无标记分割方法

成年小鼠数字截肢和再生:研究哺乳动物暴发体形成和内膜性细胞化的简单模型

半自动纵 Microcomputed Tomography-based 裸鼠骨质疏松症相关椎体骨折模型的定量结构分析

同时布莱特菲尔德， 荧光， 和光学相干断层成像收缩心脏特拉贝库莱 Ex Vivo

TACI:用于 3D 钙成像分析的 ImageJ 插件

软件辅助骨关节炎软骨下骨厚度的定量测量

自动定量分析细胞计数程序使用ImageJ插件

骨质疏松症小鼠模型中的骨小梁微观结构评估

同时布莱特菲尔德，荧光，和光学相干断层成像收缩心脏特拉贝库莱 Ex Vivo