来自2D切片的3D细胞几何结构的统计特性
Statistical properties of 3D cell geometry from 2D slices
摘要：
虽然细胞形状可以反映细胞及其环境的机械和生物化学性质，但3D组织内的3D细胞形状的量化仍然很困难，通常需要从一堆2D图像进行数字重建。我们研究了一种简单的替代技术来提取组织中细胞的3D形状的信息;该技术将组织中的3D形状的集合与在独立的2D切片中观察到的2D形状的分布连接起来。使用细胞顶点模型的几何形状，我们发现，2D形状的分布允许明确的3D形状指数的平均值的确定。我们分析了在实际中可能出现的错误，从2D图像的平均3D形状指数的估计，并发现，通常只有几十个细胞在2D图像中需要减少不确定性低于2%。这个框架可以自然地扩展到估计额外的3D几何特征，并量化它们在其他材料中的不确定性。
英文摘要：
Although cell shape can reflect the mechanical and biochemical properties of the cell and its environment, quantification of 3D cell shapes within 3D tissues remains difficult, typically requiring digital reconstruction from a stack of 2D images. We investigate a simple alternative technique to extract information about the 3D shapes of cells in a tissue; this technique connects the ensemble of 3D shapes in the tissue with the distribution of 2D shapes observed in independent 2D slices. Using cell vertex model geometries, we find that the distribution of 2D shapes allows clear determination of the mean value of a 3D shape index. We analyze the errors that may arise in practice in the estimation of the mean 3D shape index from 2D imagery and find that typically only a few dozen cells in 2D imagery are required to reduce uncertainty below 2\%. This framework could be naturally extended to estimate additional 3D geometric features and quantify their uncertainty in other materials.
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