  We propose an image-based cellular contractile force evaluation method using
a machine learning technique. We use a special substrate that exhibits wrinkles
when cells grab the substrate and contract, and the wrinkles can be used to
visualize the force magnitude and direction. In order to extract wrinkles from
the microscope images, we develop a new CNN (convolutional neural network)
architecture SW-UNet (small-world U-Net), which is a CNN that reflects the
concept of the small-world network. The SW-UNet shows better performance in
wrinkle segmentation task compared to other methods: the error (Euclidean
distance) of SW-UNet is 4.9 times smaller than 2D-FFT (fast Fourier transform)
based segmentation approach, and is 2.9 times smaller than U-Net. As a
demonstration, we compare the contractile force of U2OS (human osteosarcoma)
cells and show that cells with a mutation in the KRAS oncogne show larger force
compared to the wild-type cells. Our new machine learning based algorithm
provides us an efficient, automated and accurate method to evaluate the cell
contractile force.
