Characterization of the complete fiber network topology of planar fibrous tissues and scaffolds

Abstract

Understanding how engineered tissue scaffold architecture affects cell morphology, metabolism, phenotypic expression, as well as predicting material mechanical behavior have recently received increased attention. In the present study, an image-based analysis approach that provides an automated tool to characterize engineered tissue fiber network topology is presented. Microarchitectural features that fully defined fiber network topology were detected and quantified, which include fiber orientation, connectivity, intersection spatial density, and diameter. Algorithm performance was tested using scanning electron microscopy (SEM) images of electrospun poly(ester urethane)urea (ES-PEUU) scaffolds. SEM images of rabbit mesenchymal stem cell (MSC) seeded collagen gel scaffolds and decellularized rat carotid arteries were also analyzed to further evaluate the ability of the algorithm to capture fiber network morphology regardless of scaffold type and the evaluated size scale. The image analysis procedure was validated qualitatively and quantitatively, comparing fiber network topology manually detected by human operators (n=5) with that automatically detected by the algorithm. Correlation values between manual detected and algorithm detected results for the fiber angle distribution and for the fiber connectivity distribution were 0.86 and 0.93 respectively. Algorithm detected fiber intersections and fiber diameter values were comparable (within the mean ± standard deviation) with those detected by human operators. This automated approach identifies and quantifies fiber network morphology as demonstrated for three relevant scaffold types and provides a means to: (1) guarantee objectivity, (2) significantly reduce analysis time, and (3) potentiate broader analysis of scaffold architecture effects on cell behavior and tissue development both in vitro and in vivo.

Fibrin structural and diffusional analysis suggests that fibers are permeable to solute transport

“…On the other hand, fiber extraction algorithms have only recently been implemented for quantifying structural characteristics of fibrous networks [49,[61][62][63][64]. In this study the two techniques were combined to yield complementary quantitative data, and at the same time this This quantitative structural analysis has shown that changes in fibrinogen, thrombin, factor XIII and calcium concentrations induce significant changes in the structure of the hydrogels, both in terms of the overall network organization and individual fiber characteristics.…”

Computational modeling of the arterial wall based on layer-specific histological data

“…Recently, many efforts have been devoted to study the mechanical behavior of a single collagen fiber (Annovazzi and Genna 2010;Ogden 2011, 2013). Also, more detailed geometric information regarding the fiber microscopic arrangement can be acquired via advanced image processing technologies such as confocal reflection imaging (Roeder et al 2002;Arganda-Carreras et al 2010) or scanning electron microscopy (SEM) (D'Amore et al 2010). All these advances make it possible to obtain the material parameters completely from experiments.…”

Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images

“…First, the microstructure of many samples is highly complex [5]. For example, there are hundreds of substructures with different shapes and sizes (e.g., "dendritic structure" in Al-La alloy [6], "grains" in polycrystalline iron [2], or "cells" in biomaterials [7], etc.) which must be accurately segmented in each image.…”