Understanding how scaffold structure influences cell morphology, metabolism, phenotypic expression, and predicting mechanical behaviors have increasingly become crucial goals in the development of engineered tissue scaffolds. A novel image-based analysis algorithm that provides an automatic tool to characterize engineered tissue fiber network topology is presented. Micro architectural descriptors that unambiguously define the fiber network topology were detected, which include fiber orientation distribution, connectivity, intersection spatial density, and diameter. Algorithm performance was tested using actual sample scanning electron microscopy (SEM) images of (1) electrospun poly(ester urethane)urea (ES-PEUU) scaffolds, (2) rabbit MSCs seeded collagen gel scaffolds, and (3) decellularized rat carotid arteries. Qualitative and quantitative validation was performed comparing fiber network topology manually detected by human operators (n=5) with the one automatically detected by the algorithm. R2 correlation values defining the correlation 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 inside the (mean ± standard deviation) range detected by human operators. The algorithm’s ability to automatically identify and quantify the complete fiber network morphology regardless of the scaffold typology and of the scale of the problem was proven analyzing three different scaffold models. While the presented validation shows strong consistency between the human operators and the algorithm analysis results the automatic procedure guaranties objectivity and a significant reduction of the analysis time.
|Numero di pagine||1|
|Stato di pubblicazione||Published - 2010|