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Multiphase level set for automated delineation of membrane-bound macromolecules

机译:多相水平设置,可自动描绘膜结合的大分子

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Membrane-bound macromolecules play an important role in tissue architecture and cell-cell communication, and is regulated by almost one-third of the genome. At the optical scale, one group of membrane proteins expresses themselves as linear structures along the cell surface boundaries, while others are sequestered. This paper targets the former group, whose intensity distributions are often heterogeneous and may lack specificity. Segmentation of the membrane protein enables the quantitative assessment of localization for comparative analysis. We introduce a three-step process to (i) regularize the membrane signal through iterative tangential voting, (ii) constrain the location of surface proteins by nuclear features, and (iii) assign membrane proteins to individual cells through an application of multi-phase geodesic level-set. We have validated our method against a dataset of 200 images, and demonstrated that multiphase level set has a superior performance compared to gradient vector flow snake.
机译:膜结合的大分子在组织结构和细胞间的通讯中起着重要的作用,并受近三分之一的基因组的调控。在光学尺度上,一组膜蛋白沿着细胞表面边界表达为线性结构,而另一组则被隔离。本文针对的是前一组,后者的强度分布通常是异质的,可能缺乏特异性。膜蛋白的分割使得能够定量评估定位以进行比较分析。我们介绍了一个三步过程:(i)通过迭代切向投票使膜信号规则化;(ii)通过核特征限制表面蛋白的位置;(iii)通过应用多相将膜蛋白分配给单个细胞测地线水平集。我们已经针对200个图像的数据集验证了我们的方法,并证明了与梯度矢量流蛇相比,多相水平集具有更高的性能。

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