Improving the Segmentation of Scanning Probe Microscope Images using Convolutional Neural Networks

TL;DR

This paper presents a convolutional neural network-based segmentation method that improves the accuracy and efficiency of analyzing nanostructured surface images, specifically gold nanoparticle assemblies, outperforming traditional techniques.

Contribution

The study introduces a U-Net CNN-based segmentation protocol tailored for nanostructured surface images, demonstrating superior performance over conventional methods.

Findings

U-Net outperforms traditional segmentation approaches.

CNN-based method reduces user bias and manual effort.

Effective in processing diverse nano- and micro-structured patterns.

Abstract

A wide range of techniques can be considered for segmentation of images of nanostructured surfaces. Manually segmenting these images is time-consuming and results in a user-dependent segmentation bias, while there is currently no consensus on the best automated segmentation methods for particular techniques, image classes, and samples. Any image segmentation approach must minimise the noise in the images to ensure accurate and meaningful statistical analysis can be carried out. Here we develop protocols for the segmentation of images of 2D assemblies of gold nanoparticles formed on silicon surfaces via deposition from an organic solvent. The evaporation of the solvent drives far-from-equilibrium self-organisation of the particles, producing a wide variety of nano- and micro-structured patterns. We show that a segmentation strategy using the U-Net convolutional neural network outperforms traditional automated approaches and has particular potential in the processing of images of nanostructured systems.