Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks

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Standard

Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks. / Selvan, Raghavendra; Kipf, Thomas; Welling, Max; Juarez, Antonio Garcia-Uceda; Pedersen, Jesper H; Petersen, Jens; Bruijne, Marleen de.

I: Medical Image Analysis, Bind 64, 101751, 2020.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Selvan, R, Kipf, T, Welling, M, Juarez, AG-U, Pedersen, JH, Petersen, J & Bruijne, MD 2020, 'Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks', Medical Image Analysis, bind 64, 101751. https://doi.org/10.1016/j.media.2020.101751

APA

Selvan, R., Kipf, T., Welling, M., Juarez, A. G-U., Pedersen, J. H., Petersen, J., & Bruijne, M. D. (2020). Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks. Medical Image Analysis, 64, [101751]. https://doi.org/10.1016/j.media.2020.101751

Vancouver

Selvan R, Kipf T, Welling M, Juarez AG-U, Pedersen JH, Petersen J o.a. Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks. Medical Image Analysis. 2020;64. 101751. https://doi.org/10.1016/j.media.2020.101751

Author

Selvan, Raghavendra ; Kipf, Thomas ; Welling, Max ; Juarez, Antonio Garcia-Uceda ; Pedersen, Jesper H ; Petersen, Jens ; Bruijne, Marleen de. / Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks. I: Medical Image Analysis. 2020 ; Bind 64.

Bibtex

@article{af4d40c0839f4e4ba4f6500d77c61fb7,
title = "Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks",
abstract = "Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-trees from image data by, first deriving a graph-based representation of the volumetric data and then, posing the tree extraction as a graph refinement task. We present two methods to perform graph refinement. First, we use mean-field approximation (MFA) to approximate the posterior density over the subgraphs from which the optimal subgraph of interest can be estimated. Mean field networks (MFNs) are used for inference based on the interpretation that iterations of MFA can be seen as feed-forward operations in a neural network. This allows us to learn the model parameters using gradient descent. Second, we present a supervised learning approach using graph neural networks (GNNs) which can be seen as generalisations of MFNs. Subgraphs are obtained by training a GNN-based graph refinement model to directly predict edge probabilities. We discuss connections between the two classes of methods and compare them for the task of extracting airways from 3D, low-dose, chest CT data. We show that both the MFN and GNN models show significant improvement when compared to one baseline method, that is similar to a top performing method in the EXACT'09 Challenge, and a 3D U-Net based airway segmentation model, in detecting more branches with fewer false positives.",
keywords = "cs.CV, cs.LG, stat.ML",
author = "Raghavendra Selvan and Thomas Kipf and Max Welling and Juarez, {Antonio Garcia-Uceda} and Pedersen, {Jesper H} and Jens Petersen and Bruijne, {Marleen de}",
note = "Accepted for publication at Medical Image Analysis. 14 pages",
year = "2020",
doi = "10.1016/j.media.2020.101751",
language = "English",
volume = "64",
journal = "Medical Image Analysis",
issn = "1361-8415",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks

AU - Selvan, Raghavendra

AU - Kipf, Thomas

AU - Welling, Max

AU - Juarez, Antonio Garcia-Uceda

AU - Pedersen, Jesper H

AU - Petersen, Jens

AU - Bruijne, Marleen de

N1 - Accepted for publication at Medical Image Analysis. 14 pages

PY - 2020

Y1 - 2020

N2 - Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-trees from image data by, first deriving a graph-based representation of the volumetric data and then, posing the tree extraction as a graph refinement task. We present two methods to perform graph refinement. First, we use mean-field approximation (MFA) to approximate the posterior density over the subgraphs from which the optimal subgraph of interest can be estimated. Mean field networks (MFNs) are used for inference based on the interpretation that iterations of MFA can be seen as feed-forward operations in a neural network. This allows us to learn the model parameters using gradient descent. Second, we present a supervised learning approach using graph neural networks (GNNs) which can be seen as generalisations of MFNs. Subgraphs are obtained by training a GNN-based graph refinement model to directly predict edge probabilities. We discuss connections between the two classes of methods and compare them for the task of extracting airways from 3D, low-dose, chest CT data. We show that both the MFN and GNN models show significant improvement when compared to one baseline method, that is similar to a top performing method in the EXACT'09 Challenge, and a 3D U-Net based airway segmentation model, in detecting more branches with fewer false positives.

AB - Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-trees from image data by, first deriving a graph-based representation of the volumetric data and then, posing the tree extraction as a graph refinement task. We present two methods to perform graph refinement. First, we use mean-field approximation (MFA) to approximate the posterior density over the subgraphs from which the optimal subgraph of interest can be estimated. Mean field networks (MFNs) are used for inference based on the interpretation that iterations of MFA can be seen as feed-forward operations in a neural network. This allows us to learn the model parameters using gradient descent. Second, we present a supervised learning approach using graph neural networks (GNNs) which can be seen as generalisations of MFNs. Subgraphs are obtained by training a GNN-based graph refinement model to directly predict edge probabilities. We discuss connections between the two classes of methods and compare them for the task of extracting airways from 3D, low-dose, chest CT data. We show that both the MFN and GNN models show significant improvement when compared to one baseline method, that is similar to a top performing method in the EXACT'09 Challenge, and a 3D U-Net based airway segmentation model, in detecting more branches with fewer false positives.

KW - cs.CV

KW - cs.LG

KW - stat.ML

U2 - 10.1016/j.media.2020.101751

DO - 10.1016/j.media.2020.101751

M3 - Journal article

VL - 64

JO - Medical Image Analysis

JF - Medical Image Analysis

SN - 1361-8415

M1 - 101751

ER -

ID: 242359795