Extraction of Airways using Graph Neural Networks

Publikation: KonferencebidragKonferenceabstrakt til konferenceForskningfagfællebedømt

Standard

Extraction of Airways using Graph Neural Networks. / Raghavendra, Selvan; Kipf, Thomas ; Welling, Max; Pedersen, Jesper Johannes Holst; Petersen, Jens; de Bruijne, Marleen.

2018. Abstract fra 1st International conference on Medical Imaging with Deep Learning, Amsterdam, Holland.

Publikation: KonferencebidragKonferenceabstrakt til konferenceForskningfagfællebedømt

Harvard

Raghavendra, S, Kipf, T, Welling, M, Pedersen, JJH, Petersen, J & de Bruijne, M 2018, 'Extraction of Airways using Graph Neural Networks', 1st International conference on Medical Imaging with Deep Learning, Amsterdam, Holland, 04/07/2018 - 06/07/2018.

APA

Raghavendra, S., Kipf, T., Welling, M., Pedersen, J. J. H., Petersen, J., & de Bruijne, M. (2018). Extraction of Airways using Graph Neural Networks. Abstract fra 1st International conference on Medical Imaging with Deep Learning, Amsterdam, Holland.

Vancouver

Raghavendra S, Kipf T, Welling M, Pedersen JJH, Petersen J, de Bruijne M. Extraction of Airways using Graph Neural Networks. 2018. Abstract fra 1st International conference on Medical Imaging with Deep Learning, Amsterdam, Holland.

Author

Raghavendra, Selvan ; Kipf, Thomas ; Welling, Max ; Pedersen, Jesper Johannes Holst ; Petersen, Jens ; de Bruijne, Marleen. / Extraction of Airways using Graph Neural Networks. Abstract fra 1st International conference on Medical Imaging with Deep Learning, Amsterdam, Holland.3 s.

Bibtex

@conference{524ad3bb9bce4db8ba02ca7fa42fc4b4,
title = "Extraction of Airways using Graph Neural Networks",
abstract = "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. ",
author = "Selvan Raghavendra and Thomas Kipf and Max Welling and Pedersen, {Jesper Johannes Holst} and Jens Petersen and {de Bruijne}, Marleen",
year = "2018",
language = "English",
note = "1st International conference on Medical Imaging with Deep Learning ; Conference date: 04-07-2018 Through 06-07-2018",

}

RIS

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