A deep learned nanowire segmentation model using synthetic data augmentation

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 of high-resolution experimental images and assigning labels in order to train such algorithms is challenging and costly in terms of both time and labor expense. 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 (V 2 O 5 ) 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 V 2 O 5 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.

A Complete Strategy to Achieve High Precision Automatic Segmentation of Challenging Experimental X‐Ray Computed Tomography Data Using Low‐Resemblance Synthetic Training Data

“…[7,10] A viable solution is to employ synthetic training data. This approach was used successfully by various researchers, [4,[11][12][13] but in all cases considered, the problem did not involve highly complex microstructures (i.e., only a couple of phases present with distinct gray levels), and most studies employed a 2D DCNN.…”