Extraction of Airways using Graph Neural Networks
Research output: Contribution to conference › Conference abstract for conference › Research › peer-review
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Extraction of Airways using Graph Neural Networks. / Raghavendra, Selvan; Kipf, Thomas ; Welling, Max; Pedersen, Jesper Johannes Holst; Petersen, Jens; de Bruijne, Marleen.
2018. Abstract from 1st International conference on Medical Imaging with Deep Learning, Amsterdam, Netherlands.Research output: Contribution to conference › Conference abstract for conference › Research › peer-review
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TY - ABST
T1 - Extraction of Airways using Graph Neural Networks
AU - Raghavendra, Selvan
AU - Kipf, Thomas
AU - Welling, Max
AU - Pedersen, Jesper Johannes Holst
AU - Petersen, Jens
AU - de Bruijne, Marleen
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - https://openreview.net/forum?id=rkn2fjjjG
M3 - Conference abstract for conference
T2 - 1st International conference on Medical Imaging with Deep Learning
Y2 - 4 July 2018 through 6 July 2018
ER -
ID: 217115119