3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization

@article{James20223DCN,
  title={3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization},
  author={Jim James and Nathan Pruyne and Tiberiu Stan and Marcus Schwarting and Jiwon Yeom and Seungbum Hong and Peter Voorhees and Benjamin J. Blaiszik and Ian T. Foster},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.01167}
}
Dendritic microstructures are ubiquitous in nature and are the primary solidification morphologies in metallic materials. Techniques such as x-ray computed tomography (XCT) have provided new insights into dendritic phase transformation phenomena. However, manual identification of dendritic morphologies in microscopy data can be both labor intensive and potentially ambiguous. The analysis of 3D datasets is particularly challenging due to their large sizes (terabytes) and the presence of… 
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