A deep learned nanowire segmentation model using synthetic data augmentation

TL;DR

This paper presents a deep learning segmentation model trained solely on synthetic images that effectively identifies nanowires in real spectromicroscopy and SEM images, reducing the need for costly labeled data.

Contribution

It introduces a synthetic data augmentation approach to train Mask R-CNN for nanowire segmentation, enabling accurate analysis of experimental images without extensive manual labeling.

Findings

Model trained on synthetic data accurately segments real spectromicroscopy images.

The approach generalizes well to SEM images despite different imaging modalities.

Synthetic training data can be extended to various particle morphologies and materials.

Abstract

Automatized object identification and feature analysis of experimental image data are indispensable for data-driven material science; deep-learning-based segmentation algorithms have been shown to be a promising technique to achieve this goal. However, acquiring high-resolution experimental images and assigning labels in order to train such algorithms is challenging and costly in terms of both time and labor. In the present work, we apply synthetic images, which resemble the experimental image data in terms of geometrical and visual features, to train state-of-art deep learning-based Mask R-CNN algorithms to segment vanadium pentoxide (V2O5) nanowires, a canonical cathode material, within optical intensity-based images from spectromicroscopy. The performance evaluation demonstrates that even though the deep learning model is trained on pure synthetically generated structures, it can segment real optical intensity-based spectromicroscopy images of complex V2O5 nanowire structures in overlapped particle networks, thus providing reliable statistical information. The model can further be used to segment nanowires in scanning electron microscopy (SEM) images, which are fundamentally different from the training dataset known to the model. The proposed methodology of using a purely synthetic dataset to train the deep learning model can be extended to any optical intensity-based images of variable particle morphology, extent of agglomeration, material class, and beyond.