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MULTIPHASE LEVEL SET FOR AUTOMATED DELINEATION OF MEMBRANE-BOUND MACROMOLECULES

机译:用于自动描绘膜结合的大分子的多相级别

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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 - 细胞通信中起重要作用,并且由基因组的几乎三分之一进行调节。在光学刻度下,一组膜蛋白表达自己沿细胞表面边界的线性结构,而其他膜蛋白质被隔离。本文针对前一组,其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本地化以进行比较分析。我们将三步过程介绍至(i)通过迭代切向投票将膜信号正规化,(ii)通过核特征约束表面蛋白的位置,(iii)通过应用多相来将膜蛋白分配给单个细胞GeodeSic Level-Set。我们已经验证了我们对200个图像的数据集的方法,并证明了与梯度矢量流动蛇相比具有卓越的性能。

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