A linear programming approach to reconstructing subcellular structures from confocal images for automated generation of representative 3D cellular models.
A linear programming approach to reconstructing subcellular structures from confocal images for automated generation of representative 3D cellular models.
复制标题
一种线性编程方法，用于从共焦图像中重建亚细胞结构，用于自动生成代表性3D细胞模型。
DOI：
10.1016/j.media.2012.12.002
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发表时间：
2013-04
影响因子：
10.9
通讯作者：
Dean, Delphine
中科院分区：
文献类型：
--
作者：
Wood, Scott T.;Dean, Brian C.;Dean, Delphine
关键词：
本文提出了一种新的计算机视觉算法来分析荧光染色单细胞的3D堆叠共焦图像。该算法的目标是创建具有代表性的硅胶模型结构，这些结构可以导入有限元分析软件进行力学表征。细胞和细胞核边界的分割是通过标准的阈值方法完成的。使用新的线性规划方法，通过计算与实验3D共焦图像相比具有最小偏差的纤维的线性叠加，生成了典型的肌动蛋白应力纤维网络。定性验证是通过分析在2D培养中生长的贴壁血管平滑肌细胞(VSMC)的7个3D共聚焦图像堆栈来进行的。提出的方法能够基于标准细胞显微镜数据自动生成细胞边界、细胞核和具有代表性的F-肌动蛋白网络的3D几何图形。这些几何形状可用于在结构有限元模型中直接导入和实施，以分析单个细胞的力学，从而潜在地加速再生医学、机械生物学和药物发现领域的发现。
This paper presents a novel computer vision algorithm to analyze 3D stacks of confocal images of fluorescently stained single cells. The goal of the algorithm is to create representative in silico model structures that can be imported into finite element analysis software for mechanical characterization. Segmentation of cell and nucleus boundaries is accomplished via standard thresholding methods. Using novel linear programming methods, a representative actin stress fiber network is generated by computing a linear superposition of fibers having minimum discrepancy compared with an experimental 3D confocal image. Qualitative validation is performed through analysis of seven 3D confocal image stacks of adherent vascular smooth muscle cells (VSMCs) grown in 2D culture. The presented method is able to automatically generate 3D geometries of the cell's boundary, nucleus, and representative F-actin network based on standard cell microscopy data. These geometries can be used for direct importation and implementation in structural finite element models for analysis of the mechanics of a single cell to potentially speed discoveries in the fields of regenerative medicine, mechanobiology, and drug discovery.
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