Segmentation and detection of fluorescent 3D spots.
Segmentation and detection of fluorescent 3D spots.
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荧光 3D 斑点的分割和检测。
DOI：
10.1002/cyto.a.22017
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Bosco,Giovanni
中科院分区：
文献类型：
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作者：
Ram,Sundaresh;Rodriguez,JeffreyJ;Bosco,Giovanni
细胞核内基因和其他遗传元件的三维空间组织对于调控基因表达是重要的。了解这种空间组织是如何在细胞的整个生命过程中建立和维持的，是阐明基因调控的多个层面的关键。研究核组织的定量方法将有助于深入了解维持基因组织的分子机制，并为核结构丧失引起的病理诊断提供工具。然而，生物学家目前缺乏自动化和高通量的方法来对3D基因组织进行定量和定性的全局分析。在这项研究中，我们使用共聚焦显微镜和荧光原位杂交(FISH)作为一种细胞遗传学技术来检测和定位3D中特定DNA序列的存在。FISH使用结合到染色体上特定目标位置的探针，在使用荧光显微镜获得的3D图像中显示为荧光点。在本文中，我们提出了一种自动分割和检测3D鱼斑的算法。该算法分为两个阶段：光斑分割和光斑检测。光斑分割包括减少噪声影响的3D各向异性平滑、高帽滤波和强度阈值，然后是3D区域生长。斑点检测使用贝叶斯分类器，结合斑点的体积、平均强度、纹理和对比度等特征来检测分割出的斑点，并将其分类为真或假斑点。对所提出的算法进行的定量评估表明，与其他技术相比，该算法的分割和检测精度有所提高。2012国际细胞计数学促进会
The 3D spatial organization of genes and other genetic elements within the nucleus is important for regulating gene expression. Understanding how this spatial organization is established and maintained throughout the life of a cell is key to elucidating the many layers of gene regulation. Quantitative methods for studying nuclear organization will lead to insights into the molecular mechanisms that maintain gene organization as well as serve as diagnostic tools for pathologies caused by loss of nuclear structure. However, biologists currently lack automated and high throughput methods for quantitative and qualitative global analysis of 3D gene organization. In this study, we use confocal microscopy and fluorescence in‐situ hybridization (FISH) as a cytogenetic technique to detect and localize the presence of specific DNA sequences in 3D. FISH uses probes that bind to specific targeted locations on the chromosomes, appearing as fluorescent spots in 3D images obtained using fluorescence microscopy. In this article, we propose an automated algorithm for segmentation and detection of 3D FISH spots. The algorithm is divided into two stages: spot segmentation and spot detection. Spot segmentation consists of 3D anisotropic smoothing to reduce the effect of noise, top‐hat filtering, and intensity thresholding, followed by 3D region‐growing. Spot detection uses a Bayesian classifier with spot features such as volume, average intensity, texture, and contrast to detect and classify the segmented spots aseither true or false spots. Quantitative assessment of the proposed algorithm demonstrates improved segmentation and detection accuracy compared to other techniques. © 2012 International Society for Advancement of Cytometry