Multiphase level set for automated delineation of membrane-bound macromolecules

Hang Chang, B. Parvin
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引用次数: 8

Abstract

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.
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用于自动描述膜结合大分子的多相水平设置
膜结合大分子在组织结构和细胞间通讯中起着重要作用,几乎三分之一的基因组都对其进行调控。在光学尺度上,一组膜蛋白表现为沿着细胞表面边界的线性结构,而其他的则是隔离的。本文的目标是前一类,其强度分布往往是异质的,可能缺乏特异性。膜蛋白的分割使定位的定量评估进行比较分析。我们引入了一个三步过程来(i)通过迭代切向投票使膜信号正则化,(ii)通过核特征约束表面蛋白的位置,以及(iii)通过应用多相测地水平集将膜蛋白分配给单个细胞。我们在200张图像的数据集上验证了我们的方法,并证明了多相水平集与梯度向量流蛇相比具有更好的性能。
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