Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet

TL;DR

This paper introduces SW-UNet, a novel CNN architecture inspired by small-world networks, for accurately segmenting wrinkles in microscope images to evaluate cellular contractile forces, outperforming existing methods.

Contribution

The development of SW-UNet, a new CNN architecture that improves wrinkle segmentation accuracy for cellular force measurement, integrating small-world network concepts.

Findings

SW-UNet reduces segmentation error by 4.9 times compared to 2D-FFT.

SW-UNet outperforms standard U-Net with 2.9 times less error.

Cells with KRAS mutation exert larger contractile forces.

Abstract

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.