Farley_2021_Mach._Learn.__Sci._Technol._2_015015.pdf (5.99 MB)

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

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journal contribution
posted on 01.04.2021, 13:07 by Richard Farley, Jo EA Hodgkinson, Oliver M Gordon, Joanna Turner, Andrea Soltoggio, Philip J Moriarty, Eugenie Hunsicker
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.

History

School

  • Science

Department

  • Computer Science
  • Mathematical Sciences

Published in

Machine Learning: Science and Technology

Volume

2

Issue

1

Publisher

IOP Publishing Ltd

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by IOP Publishing under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

05/11/2020

Publication date

2020-12-24

Copyright date

2020

ISSN

2632-2153

eISSN

2632-2153

Language

en

Depositor

Dr Eugenie Hunsicker. Deposit date: 29 March 2021

Article number

015015

Licence

Exports