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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Semi-automated image processing system for micro- to macro-scale analysis of immunohistopathology: application to ischemic brain tissue.
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Semi-automated image processing system for micro- to macro-scale analysis of immunohistopathology: application to ischemic brain tissue.

机译：半自动图像处理系统，用于免疫组织病理学的微观至宏观分析：应用于缺血性脑组织。

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摘要

Immunochemical staining techniques are commonly used to assess neuronal, astrocytic and microglial alterations in experimental neuroscience research, and in particular, are applied to tissues from animals subjected to ischemic stroke. Immunoreactivity of brain sections can be measured from digitized immunohistology slides so that quantitative assessment can be carried out by computer-assisted analysis. Conventional methods of analyzing immunohistology are based on image classification techniques applied to a specific anatomic location at high magnification. Such micro-scale localized image analysis limits one for further correlative studies with other imaging modalities on whole brain sections, which are of particular interest in experimental stroke research. This report presents a semi-automated image analysis method that performs convolution-based image classification on micro-scale images, extracts numerical data representing positive immunoreactivity from the processed micro-scale images and creates a corresponding quantitative macro-scale image. The present method utilizes several image-processing techniques to cope with variances in intensity distribution, as well as artifacts caused by light scattering or heterogeneity of antigen expression, which are commonly encountered in immunohistology. Micro-scale images are composed by a tiling function in a mosaic manner. Image classification is accomplished by the K-means clustering method at the relatively low-magnification micro-scale level in order to increase computation efficiency. The quantitative macro-scale image is suitable for correlative analysis with other imaging modalities. This method was applied to different immunostaining antibodies, such as endothelial barrier antigen (EBA), lectin, and glial fibrillary acidic protein (GFAP), on histology slides from animals subjected to middle cerebral artery occlusion by the intraluminal suture method. Reliability tests show that the results obtained from immunostained images at high magnification and relatively low magnification are virtually the same.
机译：免疫化学染色技术通常用于评估实验神经科学研究中的神经元，星形胶质细胞和小胶质细胞改变，特别是应用于缺血性中风动物的组织。可以从数字化的免疫组织学切片测量大脑切片的免疫反应性，以便可以通过计算机辅助分析进行定量评估。分析免疫组织学的常规方法是基于图像分类技术，该图像分类技术以高放大倍率应用于特定的解剖位置。这种微尺度的局部图像分析限制了其与全脑切片上其他成像方式的进一步相关性研究，这在实验性卒中研究中尤为重要。该报告提出了一种半自动图像分析方法，该方法在微尺度图像上执行基于卷积的图像分类，从处理的微尺度图像中提取代表阳性免疫反应性的数值数据，并创建相应的定量宏尺度图像。本方法利用几种图像处理技术来应对强度分布的变化，以及由免疫组织学中常见的由光散射或抗原表达异质性引起的伪影。微型图像由平铺功能以镶嵌方式组成。为了提高计算效率，通过K-means聚类方法在相对较低的放大倍数下对图像进行分类。定量宏观图像适合与其他成像方式进行相关分析。该方法适用于不同的免疫染色抗体，如内皮屏障抗原（EBA），凝集素和神经胶质原纤维酸性蛋白（GFAP），通过腔内缝合法从大脑中动脉闭塞的动物的组织学切片中提取。可靠性测试表明，从高倍和相对低倍的免疫染色图像获得的结果实际上是相同的。

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