A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs

@article{Juarez2019AJ3,
  title={A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs},
  author={A. Garcia-Uceda Juarez and Raghavendra Selvan and Zaigham Saghir and Marleen de Bruijne},
  journal={ArXiv},
  year={2019},
  volume={abs/1908.08588}
}
We present an end-to-end deep learning segmentation method by combining a 3D UNet architecture with a graph neural network (GNN) model. In this approach, the convolutional layers at the deepest level of the UNet are replaced by a GNN-based module with a series of graph convolutions. The dense feature maps at this level are transformed into a graph input to the GNN module. The incorporation of graph convolutions in the UNet provides nodes in the graph with information that is based on node… Expand
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