Understanding how engineered tissue scaffold architecture affects cell morphology, metabolism,phenotypic expression, as well as predicting material mechanical behavior has recently receivedincreased attention. In the present study, an image-based analysis approach that provides an automatedtool to characterize engineered tissue fiber network topology is presented. Micro-architectural featuresthat fully defined fiber network topology were detected and quantified, which include fiber orientation,connectivity, intersection spatial density, and diameter. Algorithm performance was tested using scanningelectron 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 ratcarotid arteries were also analyzed to further evaluate the ability of the algorithm to capture fibernetwork morphology regardless of scaffold type and the evaluated size scale. The image analysisprocedure was validated qualitatively and quantitatively, comparing fiber network topology manuallydetected by human operators (n ¼ 5) with that automatically detected by the algorithm. Correlationvalues between manual detected and algorithm detected results for the fiber angle distribution and forthe fiber connectivity distribution were 0.86 and 0.93 respectively. Algorithm detected fiber intersectionsand fiber diameter values were comparable (within the mean standard deviation) with those detectedby human operators. This automated approach identifies and quantifies fiber network morphology asdemonstrated 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 oncell behavior and tissue development both in vitro and in vivo.
|Numero di pagine||11|
|Stato di pubblicazione||Published - 2010|
All Science Journal Classification (ASJC) codes