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Segmentation of microspheres in ultrahigh density multiplexed microsphere-based assays

机译:基于超高密度多重微球的分析中的微球分割

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We have developed a method to identify and localize luminescent microspheres in dense images of microsphere-based assays. Application of this algorithm to the images of densely packed microspheres would aid in increasing the number of assays per unit target sample volume by several orders of magnitude. We immobilize or sediment microspheres on microscope slides and read luminescence from these randomly arrayed microspheres with a digital imaging microscope equipped with a cooled CCD camera. Our segmentation algorithm, which is based on marker-controlled watershed transformation, is then implemented to segment the microsphere clusters in the luminescent images acquired at different wavelengths. This segmentation algorithm is fully automated and require no manual intervention or training sets for optimizing the parameters and is much more accurate than previously proposed algorithms. Using this algorithm, we have accurately segmented more than 97% of the microspheres in dense images.
机译:我们已经开发出一种方法,可以在基于微球的检测的密集图像中识别和定位发光微球。将该算法应用于密集堆积的微球的图像将有助于将每单位目标样品体积的测定数量增加几个数量级。我们将微球固定或沉积在显微镜载玻片上,并使用配备有冷却CCD相机的数字成像显微镜从这些随机排列的微球中读取发光。然后,我们基于标记控制的分水岭变换的分割算法被实现,以分割在不同波长下获取的发光图像中的微球簇。该分割算法是完全自动化的,不需要人工干预或训练集即可优化参数,并且比以前提出的算法要准确得多。使用此算法,我们已经准确地分割了密集图像中97%以上的微球。

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