Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet
This provides an efficient, automated, and accurate method for biologists studying cellular mechanics, though it is incremental as it builds on existing U-Net architectures.
The authors tackled the problem of evaluating cellular contractile forces by developing SW-UNet, a CNN architecture inspired by small-world networks for wrinkle segmentation from microscope images, which reduced error by 4.9 times compared to a 2D-FFT method and 2.9 times compared to U-Net, and demonstrated its use by showing KRAS-mutant cells exert larger forces than wild-type cells.
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.