Robust detection and visualization of cytoskeletal structures in fibrillar scaffolds from 3-dimensional confocal image

Abstract

Polymerized actin-based cytoskeletal structures provide the cells with shape, resilience and dynamics. A mechanistic understanding of actin-based structures is crucial for finding solutions to practical problems occurring in tissue engineering constructs that require the interaction of cells with materials. In this regard, the first step is to detect and quantify actin-based structures in 3D cellular ensembles. In this work, we propose visual-analytic tools to delineate specific structures involving F-actin in cells. Concave actin bundles (CABs) often occur in hybrid cell-seeded fibrillar scaffolds and seem to envelope the fibers, as a possible mechanism of stable attachment. There is much uncertainty that accompanies the detection and the identification of fibers. Our tools rely on well-known algorithms of image analysis. We first delineate fibers by employing an adaptive min-cut-max-flow algorithm. Then, from the extremities of the segmented fibers, a template matching and a fiber tracking algorithm is applied to more precisely characterize the fibers in the image. CABs that surround the scaffold fibers transversally are located by observing their radial distribution around the nearby templates in focus. Finally, we visually examine candidate templates that possibly contain CABs and further determine if candidate CABs are indeed legitimate. It can be unequivocally stated that in the absence of the proposed visual analytic tools, the detection of CABs is intractable tasks. © 2013 IEEE.