A new method: Characterize and quantify biofilm wrinkles by UNet and Sholl Analysis

Abstract

The wrinkles on the biofilm contain a lot of information about biofilm growth, so it is essential to characterize and quantify these wrinkles from the original microscopic images to discover more rules governing the biofilm morphology evolution. However, the existing methods to extract the wrinkles are time-consuming, error-prone, and require manual calibration. We propose a new system: using a deep learning method - UNet to identify the biofilm wrinkles in the original experimental images, which can achieve fast and accurate extraction of wrinkles on biofilms. Combining the result of UNet and medical neuron analysis method - Sholl Analysis, we can easily characterize and quantity the B. subtilis biofilm wrinkles. We proposed new characterization parameters such as wrinkle density, wrinkle length, and wrinkle projection area, which can precisely partition the biofilm surface wrinkles into different regions from the biofilm center to the edge, different regions correspond to different growth stages. Our system can be applied to study biofilms growing in different kinds of environments and to study the biofilm growth mechanisms.

Keywords: Biofilm wrinkle; Deep learning; Sholl analysis; UNet.