Analysis of PCIS smooth muscle contractions
Statistical analysis was
conducted using GraphPad Prism 6 (GraphPad Software, San Diego, CA,
USA). Responses between different stimulations were compared using the
Mann-Whitney U test with a P value <.05 considered
statistically significant.
A novel video analysis software was developed internally (available at:
https://github.com/celalp/video_parser). This software measures
muscle contraction using individual pixel movement per frame (Figure 2).
Videos were analyzed using a semi-supervised approach with variables
kept consistent between the unstimulated control and stimulated sample.
Videos were processed using OpenCV (Open Source Computer Vision Library)
and scikit-image Python packages.28,29 Pixels that
changed from frame x-1 to frame x were calculated using a Gaussian
mixture-based background/foreground segmentation
algorithm.30,31 A background subtraction algorithm was
chosen for this analysis as videos consisted of one large central object
with a mostly static background, meaning that any observed movement was
captured as foreground. The selection of the specific algorithm (MOG2 in
OpenCV) was influenced by the large variability of exposure levels
(e.g., glare) between different slices. A dynamic Gaussian Mixture
Method allowed for the same background removal method for all
experiments. Measurements were validated and confirmed by an independent
assessor. Overall pixel movement per frame of the video was plotted for
each stimulation and total Area Under the Curve (AUC) was calculated and
compared for each contraction response using GraphPad Prism 6.