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Trainable segmentation for transmission electron microscope images of inorganic nanoparticles
Author(s) -
Bell Cameron G.,
Treder Kevin P.,
Kim Judy S.,
Schuster Manfred E.,
Kirkland Angus I.,
Slater Thomas J. A.
Publication year - 2022
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.13110
Subject(s) - transmission electron microscopy , electron microscope , segmentation , materials science , nanoparticle , transmission (telecommunications) , nanotechnology , microscope , multiphoton fluorescence microscope , optics , conventional transmission electron microscope , computer science , scanning transmission electron microscopy , artificial intelligence , physics , fluorescence microscope , fluorescence , telecommunications
We present a trainable segmentation method implemented within the python package ParticleSpy. The method takes user labelled pixels, which are used to train a classifier and segment images of inorganic nanoparticles from transmission electron microscope images. This implementation is based on the trainable Waikato Environment for Knowledge Analysis (WEKA) segmentation, but is written in python, allowing a large degree of flexibility and meaning it can be easily expanded using other python packages. We find that trainable segmentation offers better accuracy than global or local thresholding methods and requires as few as 100 user‐labelled pixels to produce an accurate segmentation. Trainable segmentation presents a balance of accuracy and training time between global/local thresholding and neural networks, when used on transmission electron microscope images of nanoparticles. We also quantitatively investigate the effectiveness of the components of trainable segmentation, its filter kernels and classifiers, in order to demonstrate the use cases for the different filter kernels in ParticleSpy and the most accurate classifiers for different data types. A set of filter kernels is identified that are effective in distinguishing particles from background but that retain dissimilar features. In terms of classifiers, we find that different classifiers perform optimally for different image contrast; specifically, a random forest classifier performs best for high‐contrast ADF images, but that QDA and Gaussian Naïve Bayes classifiers perform better for low‐contrast TEM images.