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 D2) 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 D2, an open-source software, is available for everyone
at the following link: .