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- Title
Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM).
- Authors
Torbati-Sarraf, Hamidreza; Niverty, Sridhar; Singh, Rajhans; Barboza, Daniel; De Andrade, Vincent; Turaga, Pavan; Chawla, Nikhilesh
- 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.
- Subjects
X-ray microscopy; IMAGE segmentation; FEATURE selection; CONVOLUTIONAL neural networks; X-ray imaging; ALGORITHMS
- Publication
JOM: The Journal of The Minerals, Metals & Materials Society (TMS), 2021, Vol 73, Issue 7, p2173
- ISSN
1047-4838
- Publication type
Article
- DOI
10.1007/s11837-021-04706-x