Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM)

Hamidreza Torbati-Sarraf, Sridhar Niverty, Rajhans Singh, Daniel Barboza, Vincent De Andrade, Pavan Turaga, Nikhilesh Chawla

Research output: Contribution to journalArticlepeer-review

Abstract

Four state-of-the-art Deep Learning-based Convolutional Neural Networks (DCNN) were applied to automate the semantic segmentation of a 3D Transmission x-ray Microscopy (TXM) nanotomography image data. The standard U-Net architecture as baseline along with UNet++, PSPNet, and DeepLab v3+ networks were trained to segment the microstructural features of an AA7075 micropillar. A workflow was established to evaluate and compare the DCNN prediction dataset with the manually segmented features using the Intersection of Union (IoU) scores, time of training, confusion matrix, and visual assessment. Comparing all model segmentation accuracy metrics, it was found that using pre-trained models as a backbone along with appropriate training encoder–decoder architecture of the Unet++ can robustly handle large volumes of x-ray radiographic images in a reasonable amount of time. This opens a new window for handling accurate and efficient image segmentation of in situ time-dependent 4D x-ray microscopy experimental datasets.

Original languageEnglish (US)
Pages (from-to)2173-2184
Number of pages12
JournalJOM
Volume73
Issue number7
DOIs
StatePublished - Jul 2021

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

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