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2018

Extraction of Airways using Graph Neural Networks

Selvan, Raghavendra, Kipf, Thomas, Welling, Max et al.

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We present extraction of tree structures, such as airways, from image data as a graph refinement task.

To this end, we propose a graph auto-encoder model that uses an encoder based on graph neural networks (GNNs) to learn embeddings from input node features and a decoder to predict connections between nodes.
Performance of the GNN model is compared with mean-field networks in their ability to extract airways from 3D chest CT scans.

Extraction of Airways using Graph Neural Networks · Around

Built on

The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
Earlier work this paper cites.
The danish randomized lung cancer CT screening trial—overall design and results of the prevalence round
Jesper H Pedersen, Haseem Ashraf, Dirksen, and et.al · 2009
Earlier work this paper cites.
Airway tree extraction with locally optimal paths
Pechin Lo, Jon Sporring, Jesper Pedersen, and et.al · 2009
Earlier work this paper cites.
Mean-field networks
Original
Yujia Li and Richard Zemel · 2014
Earlier work this paper cites.

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Gated graph sequence neural networks
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Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
Cited alongside, same era.
Variational graph auto-encoders
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Extraction of airways with probabilistic state-space models and Bayesian smoothing
Raghavendra Selvan, Jens Petersen, Jesper H Pedersen, and et.al · 2017
Cited alongside, same era.

Then

Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Later among the works it cites.
Mean field network based graph refinement with application to airway tree extraction
Original
Raghavendra Selvan, Max Welling, Jens Petersen, Jesper H Pedersen, and Marleen de Bruijne · 2018
Closest in time.
Inference in probabilistic graphical models by graph neural networks
Original
KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, and et.al · 2018
Closest in time.

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