A linear programming approach to reconstructing subcellular structures from confocal images for automated generation of representative 3D cellular models.

ABSTRACT: 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

SUBMITTER: Wood ST 

PROVIDER: S-EPMC3626120 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature