Quantitative Evaluation of the Pore and Window Sizes of Tissue Engineering Scaffolds on Scanning Electron Microscope Images Using Deep Learning.

The morphological characteristics of tissue engineering scaffolds, such as pore and window diameters, are crucial, as they directly impact cell-material interactions, attachment, spreading, infiltration of the cells, degradation rate and the mechanical properties of the scaffolds. Scanning electron microscopy (SEM) is one of the most commonly used techniques for characterizing the microarchitecture of tissue engineering scaffolds due to its advantages, such as being easily accessible and having a short examination time. However, SEM images provide qualitative data that need to be manually measured using software such as ImageJ to quantify the morphological features of the scaffolds. As it is not practical to measure each pore/window in the SEM images as it requires extensive time and effort, only the number of pores/windows is measured and assumed to represent the whole sample, which may cause user bias. Additionally, depending on the number of samples and groups, a study may require measuring thousands of samples and the human error rate may increase. To overcome such problems, in this study, a deep learning model (Pore D 2 ) was developed to quantify the morphological features (such as the pore size and window size) of the open-porous scaffolds automatically for the first time. The developed algorithm was tested on emulsion-templated scaffolds fabricated under different fabrication conditions, such as changing mixing speed, temperature, and surfactant concentration, which resulted in scaffolds with various morphologies. Along with the developed model, blind manual measurements were taken, and the results showed that the developed tool is capable of quantifying pore and window sizes with a high accuracy. Quantifying the morphological features of scaffolds fabricated under different circumstances and controlling these features enable us to engineer tissue engineering scaffolds precisely for specific applications. Pore D 2 , an open-source software, is available for everyone at the following link: https://github.com/ilaydakaraca/PoreD2.