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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References

SHOWING 1-10 OF 46 REFERENCES
Segmentation of Experimental Datasets Via Convolutional Neural Networks Trained on Phase Field Simulations
TLDR
It is shown that it is possible to segment experimental materials science data using a SegNet-based CNN that was trained only using simple phase field simulations, and the CNN trained on phase field images segmented the experimental test image with 99% accuracy.
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
TLDR
This work proposes an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network, trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once.
U-Net: Convolutional Networks for Biomedical Image Segmentation
TLDR
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
TLDR
The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts and performs on-the-fly elastic deformations for efficient data augmentation during training.
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
TLDR
This work creates an open-source auto-differentiation library for sparse tensors that provides extensive functions for high-dimensional convolutional neural networks and proposes the hybrid kernel, a special case of the generalized sparse convolution, and trilateral-stationary conditional random fields that enforce spatio-temporal consistency in the 7D space-time-chroma space.
Fully Convolutional Networks for Semantic Segmentation
TLDR
It is shown that convolutional networks by themselves, trained end- to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation
TLDR
This paper proposes an approach for directly optimizing this intersection-over-union (IoU) measure in deep neural networks and demonstrates that this approach outperforms DNNs trained with standard softmax loss.
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
TLDR
The proposed DenseNets approach achieves state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining, and has much less parameters than currently published best entries for these datasets.
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