Automatic quantification of angiogenesis in 2D sections: a precise and timesaving approach

The standardized characterization of angiogenesis is crucial in the field of tissue engineering as sufficient blood supply is the limiting factor of mass transfer. However, reliable algorithms that provide a straight forward and observer-independent assessment of new vessel formation are still lacking. We propose an automatic observer-independent quantitative method (including downloadable source code) to analyze vascularization using two-dimensional microscopic images of histological cross-sections and advanced postprocessing, based on a 'positive- and negative-experts' model and a (corrected) nearest neighbour classification, in a vascularized tissue engineering model.

An established angioinductive rat arteriovenous loop model was used to compare the new automatic analysis with a common 2D method and a μCT algorithm. Angiogenesis was observed at three different time points (5, 10 and 15 days).

In line with previous results, formation of functional new vessels that arose from the venous graft was evident within the three-dimensional construct and a significant (p < 0.05) increase in vessel count and area was observed over time. The proposed automatic analysis obtained precise values for vessel count and vessel area that were similar to the manually gained data. The algorithm further provided vectorized parameterization of the newly formed vessels for advanced statistical analysis. Compared to the μCT-based three-dimensional analyses, the presented two-dimensional algorithm was superior in terms of small vessel detection as well as cost and time efficiency.

The quantitative evaluation method, using microscopic images of stained histological sections, 'positive- and negative-experts'-based vessel segmentation, and nearest neighbour classification, provides a user-independent and precise but also time- and cost-effective tool for the analysis of vascularized constructs. Our algorithm, which is freely available to the public, outperforms previous approaches especially in terms of unambiguous vessel classification and statistical analyses.