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Image quantification of high-throughput tissue microarray

机译:高通量组织芯片的图像定量

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摘要

Tissue microarray (TMA) technology allows rapid visualization of molecular targets in thousands of tissue specimens at a time and provides valuable information on expression of proteins within tissues at a cellular and sub-cellular level. TMA technology overcomes the bottleneck of traditional tissue analysis and allows it to catch up with the rapid advances in lead discovery. Studies using TMA on immunohistochemistry (IHC) can produce a large amount of images for interpretation within a very short time. Manual interpretation does not allow accurate quantitative analysis of staining to be undertaken. Automatic image capture and analysis has been shown to be superior to manual interpretation. The aims of this work is to develop a truly high-throughput and fully automated image capture and analysis system. We develop a robust colour segmentation algorithm using hue-saturation-intensiry (HSI) colour space to provide quantification of signal intensity and partitioning of staining on high-throughput TMA. Initial segmentation results and quantification data have been achieved on 16,000 TMA colour images over 23 different tissue types.
机译:组织微阵列(TMA)技术可一次快速观察数千个组织样本中的分子靶标,并提供有关细胞内和亚细胞水平组织内蛋白质表达的有价值的信息。 TMA技术克服了传统组织分析的瓶颈,并使其赶上了潜在客户发现的快速发展。使用TMA进行免疫组织化学(IHC)的研究可以在很短的时间内生成大量图像以供解释。手动解释不允许对染色进行准确的定量分析。事实表明,自动图像捕获和分析优于手动解释。这项工作的目的是开发一个真正的高通量和全自动图像捕获和分析系统。我们使用色相饱和度(HSI)颜色空间开发了一种鲁棒的颜色分割算法,以提供信号强度的量化和高通量TMA上的染色分区。在23种不同组织类型的16,000个TMA彩色图像上已经获得了初步的分割结果和定量数据。

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