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We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation.
T.-C. Lee, R. L. Kashyap, and C.-N. Chu, “Building skeleton models via 3-d medial surface axis thinning algorithms,” CVGIP: Graphical Models and Image Processing , vol. 56, no. 6, pp. 462–478, 1994
1994
Earlier work this paper cites.
H. Kitaoka et al. , “Automated nomenclature labeling of the bronchial tree in 3D-CT lung images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2002, pp. 1–11
2002
Earlier work this paper cites.
J. Tschirren, G. McLennan, K. Palágyi, E. A. Hoffman, and M. Sonka, “Matching and anatomical labeling of human airway tree,” IEEE transactions on medical imaging , vol. 24, no. 12, pp. 1540–1547, 2005
2005
Earlier work this paper cites.
T. Bülow, C. Lorenz, R. Wiemker, and J. Honko, “Point based methods for automatic bronchial tree matching and labeling,” in Medical Imaging 2006: Physiology, Function, and Structure from Medical Images , vol. 6143. International Society for Optics and Photonics, 2006, p. 61430O
2006
Earlier work this paper cites.
B. van Ginneken, W. Baggerman, and E. M. van Rikxoort, “Robust segmentation and anatomical labeling of the airway tree from thoracic CT scans,” in International Conference on Medical Image Computing and Computer-Assisted Intervention , 2008, pp. 219–226
2008
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of machine learning research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
K. Mori et al. , “Automated anatomical labeling of bronchial branches extracted from CT datasets based on machine learning and combination optimization and its application to bronchoscope guidance,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2009, pp. 707–714
2009
Earlier work this paper cites.
E. A. Regan et al. , “Genetic epidemiology of COPD (COPDGene) study design,” COPD: Journal of Chronic Obstructive Pulmonary Disease , vol. 7, pp. 32–43, 2010
2010
Earlier work this paper cites.
A. Feragen et al. , “An airway tree-shape model for geodesic airway branch labeling,” in Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy-Geometrical and Statistical Methods for Modelling Biological Shape Variability , 2011, pp. 123–134
2011
Earlier work this paper cites.
P. Lo, E. M. van Rikxoort, J. Goldin, F. Abtin, M. de Bruijne, and M. Brown, “A bottom-up approach for labeling of human airway trees,” MICCAI Int. WS. Pulm. Im. Anal , 2011
2011
Earlier work this paper cites.
D. K. Hammond, P. Vandergheynst, and R. Gribonval, “Wavelets on graphs via spectral graph theory,” Applied and Computational Harmonic Analysis , vol. 30, no. 2, pp. 129–150, 2011
2011
Earlier work this paper cites.
A. Feragen, J. Petersen, M. Owen, P. Lo, L. H. Thomsen, M. M. Wille, A. Dirksen, and M. de Bruijne, “A hierarchical scheme for geodesic anatomical labeling of airway trees,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2012, pp. 147–155
2012
Earlier work this paper cites.
A. Feragen et al. , “Geodesic atlas-based labeling of anatomical trees: Application and evaluation on airways extracted from CT,” IEEE transactions on medical imaging , vol. 34, no. 6, pp. 1212–1226, 2014
2014
Earlier work this paper cites.
J. C. Ross et al. , “Airway labeling using a hidden markov tree model,” in 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2014, pp. 554–558
2014
Cited alongside, same era.
E. S. Wan et al. , “Epidemiology, genetics, and subtyping of preserved ratio impaired spirometry (PRISm) in COPDGene,” Respiratory research , vol. 15, no. 1, p. 89, 2014
2014
Cited alongside, same era.
D. Adeloye et al. , “Global and regional estimates of COPD prevalence: Systematic review and meta–analysis,” Journal of global health , vol. 5, no. 2, 2015
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1026–1034
2015
Cited alongside, same era.
H. Hu, Z. Zhang, Z. Xie, and S. Lin, “Local relation networks for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2019, pp. 3464–3473
2019
Later among the works it cites.
A. Paszke et al. , “Pytorch: An imperative style, high-performance deep learning library,” pp. 8024–8035, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2020
Later among the works it cites.
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2016
Cited alongside, same era.
2017
Cited alongside, same era.
A. Vaswani et al. , “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , 2017, pp. 1025–1035
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Q. Li, Z. Han, and X.-M. Wu, “Deeper insights into graph convolutional networks for semi-supervised learning,” in Thirty-Second AAAI conference on artificial intelligence , 2018
2018
Cited alongside, same era.
S. Y. Shin, S. Lee, I. D. Yun, and K. M. Lee, “Deep vessel segmentation by learning graphical connectivity,” Medical image analysis , vol. 58, p. 101556, 2019
2019
Cited alongside, same era.
A. G.-U. Juarez, R. Selvan, Z. Saghir, and M. de Bruijne, “A joint 3D UNet-graph neural network-based method for airway segmentation from chest CTs,” in International workshop on machine learning in medical imaging . Springer, 2019, pp. 583–591
2019
Cited alongside, same era.
2020
Later among the works it cites.
M. Liu, H. Gao, and S. Ji, “Towards deeper graph neural networks,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 338–348
2020
Later among the works it cites.
M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li, “Simple and deep graph convolutional networks,” in International Conference on Machine Learning . PMLR, 2020, pp. 1725–1735
2020
Later among the works it cites.
W. Xie, C. Jacobs, J.-P. Charbonnier, and B. van Ginneken, “Relational modeling for robust and efficient pulmonary lobe segmentation in CT scans,” IEEE transactions on medical imaging , vol. 39, no. 8, pp. 2664–2675, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
Z. Tan, J. Feng, and J. Zhou, “SGNet: Structure-aware graph-based network for airway semantic segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2021, pp. 153–163
2021
Later among the works it cites.
2021
Later among the works it cites.