Characterizing Cell Populations Using Statistical Shape Modes
Characterizing Cell Populations Using Statistical Shape Modes
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DOI：
10.1109/isbi52829.2022.9761679
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Ximu Deng;Rituparna Sarkar;E. Labruyère;Jean-Christophe Olivo-Marin;A. Srivastava
中科院分区：
文献类型：
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作者：
Ximu Deng;Rituparna Sarkar;E. Labruyère;Jean-Christophe Olivo-Marin;A. Srivastava
我们考虑的问题，使用高频率的代表性形状的形状人口的特征。框架等形状的统计模式-形状对应于（显着）的局部最大值的基础PDF-我们开发了一个基于频率的，非参数的方法来估计样本模式。使用弹性形状度量，我们定义的形状空间和入围形状的中心，并有最多的邻居的邻域。一个关键问题-如何自动选择阈值？- 使用ANOVA和经验模式分布的组合来解决。由此产生的模态集，反过来，有助于表征形状人口和性能优于传统的聚类方法。我们证明了这个框架使用阿米巴形状从明场显微镜图像，并强调其优势，现有的想法。
We consider the problem of characterizing shape populations using highly frequent representative shapes. Framing such shapes as statistical modes – shapes that correspond to (significant) local maxima of the underlying pdfs – we develop a frequency-based, nonparametric approach for estimating sample modes. Using an elastic shape metric, we define ϵ-neighborhoods in the shape space and shortlist shapes that are central and have the most neighbors. A critical issue – How to automatically select the threshold ϵ? – is resolved using a combination of ANOVA and empirical mode distribution. The resulting modal set, in turn, helps characterize the shape population and performs better than the traditional cluster means. We demonstrate this framework using amoeba shapes from brightfield microscopy images and highlight its advantages over existing ideas.