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

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.Comment: 21 pages, 10 figure

Automated Searching and Identification of Self-Organized Nanostructures

Currently, researchers spend significant time manually searching through large volumes of data produced during scanning probe imaging to identify specific patterns and motifs formed via self-assembly and self-organisation. Here, we use a combination of Monte Carlo simulations, general statistics and machine learning to automatically distinguish several spatially-correlated patterns in a mixed, highly varied dataset of real AFM images of self-organised nanoparticles. We do this regardless of feature-scale and without the need for manually labelled training data. Provided that the structures of interest can be simulated, the strategy and protocols we describe can be easily adapted to other self-organised systems and datasets

Characterising the geometry of image space of nanostructures for inferential analysis of dewetting processes and computational models

The self-assembly of nanostructures has been of growing interest in materials science, with particular advancements in the development of computational models that describe this self-assembly. So far, however, the utility of these models have been limited by the absence of methods to relate real experimental data with numerical simulations or the experimental and simulation conditions that generate them. We have 2625 real atomic force microscope (AFM) gray-scale images of nanoparticle depositions produced through dewetting experiments and two computational models that simulate these experiments; a kinetic Monte Carlo (KMC) model and a dynamical density functional theory (DDFT) model. In this thesis, we first propose an automated neural network segmentation method to minimise noise in the real images and allow meaningful comparison with simulated images. We then characterise the geometry of the image space through defining a map to a feature space. The statistics we use as coordinates in this space are novel modifications of the Minkowski functionals. This space provides evidence of the possibility of meaningful comparison between real and simulated images. The modified Minkowski functionals are then used to make quantitative descriptions of the behaviour of the computational models. Finally, we are able to fit accurate predictive models of the types of structures we expect to see from given simulation conditions. We discuss the promise this shows for successfully carrying out the inverse problem but note that the modified Minkowski functionals are insufficient for this task by themselves and consider Riemannian geometry as a more suitable approach

Improving the segmentation of scanning probe microscope images using convolutional neural networks

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