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Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images

25 Mar 2022arXiv:2203.13875archive 2025-07-28

Shizhao Lu, Brian Montz, Todd Emrick, Arthi Jayaraman

In the field of materials science, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine learning methods that can automate the analysis and interpretation of microscopy images. Typically training of machine learning models requires large numbers of images with associated structural labels, however, manual labeling of images requires domain knowledge and is prone to human error and subjectivity. To overcome these limitations, we present a semi-supervised transfer learning approach that uses a small number of labeled microscopy images for training and performs as effectively as methods trained on significantly larger image datasets. Specifically, we train an image encoder with unlabeled images using self-supervised learning methods and use that encoder for transfer learning of different downstream image tasks (classification and segmentation) with a minimal number of labeled images for training. We test the transfer learning ability of two self-supervised learning methods: SimCLR and Barlow-Twins on transmission electron microscopy (TEM) images. We demonstrate in detail how this machine learning workflow applied to TEM images of protein nanowires enables automated classification of nanowire morphologies (e.g., single nanowires, nanowire bundles, phase separated) as well as segmentation tasks that can serve as groundwork for quantification of nanowire domain sizes and shape analysis. We also extend the application of the machine learning workflow to classification of nanoparticle morphologies and identification of different type of viruses from TEM images.

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Tasks

BIG-bench Machine Learning Instance Segmentation Morphology classification Self-Supervised Learning Transfer Learning

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TEM nanowire morphologies for classification and segmentation

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Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block ColorJitter Convolution Dense Connections Feedforward Network Global Average Pooling Kaiming Initialization Max Pooling NT-Xent Random Gaussian Blur Random Resized Crop ReLU Residual Block Residual Connection SimCLR

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