A U-Net-based approach for automated segmentation of scanning artifacts in multi-modal cellular AFM images
Here we present a method for the automatic segmentation of imaging artifacts in atomic force microscopy (AFM) maps collected on cell surfaces. The method is based on a U-Net convolutional neural network architecture and uses multi-channel AFM data, including height, adhesion, and restored adhesion maps acquired in Peak...