Fetching the paper…
Reading the bibliography…
Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases.
B. Raasch, E. Carsky, E. Lane, J. O’callaghan, and E. Heitzman, “Radiographic anatomy of the interlobar fissures: a study of 100 specimens,” AJR Am J Roentgenol , vol. 138, no. 6, pp. 1043–1049, 1982
1982
Earlier work this paper cites.
M. Kubo, N. Niki, S. Nakagawa, K. Eguchi, M. Kaneko, N. Moriyama, H. Omatsu, R. Kakinuma, and N. Yamaguchi, “Extraction algorithm of pulmonary fissures from thin-section CT images based on linear feature detector method,” IEEE Trans Nucl Sci , vol. 46, no. 6, pp. 2128–2133, 1999
1999
Earlier work this paper cites.
M. Kubo, N. Niki, K. Eguchi, M. Kaneko, M. Kusumoto, N. Moriyama, H. Omatsu, R. Kakinuma, H. Nishiyama, K. Mori et al. , “Extraction of pulmonary fissures from thin-section CT images using calculation of surface-curvatures and morphology filters,” in ICIP , vol. 2, 2000, pp. 637–640
2000
Earlier work this paper cites.
2004
Earlier work this paper cites.
J.-M. Kuhnigk, V. Dicken, S. Zidowitz, L. Bornemann, B. Kuemmerlen, S. Krass, H.-O. Peitgen, S. Yuval, H.-H. Jend, W. S. Rau et al. , “New tools for computer assistance in thoracic CT. part 1. functional analysis of lungs, lung lobes, and bronchopulmonary segments,” Radiographics , vol. 25, no. 2, pp. 525–536, 2005
2005
Earlier work this paper cites.
J. Wang, M. Betke, and J. P. Ko, “Pulmonary fissure segmentation on CT,” Med Image Anal , vol. 10, no. 4, pp. 530–547, 2006
2006
Earlier work this paper cites.
E. M. van Rikxoort, B. van Ginneken, M. Klik, and M. Prokop, “Supervised enhancement filters: application to fissure detection in chest CT scans,” IEEE Trans Med Imaging , vol. 27, no. 1, pp. 1–10, 2007
2007
Earlier work this paper cites.
J. Pu, J. K. Leader, B. Zheng, F. Knollmann, C. Fuhrman, F. C. Sciurba, and D. Gur, “A computational geometry approach to automated pulmonary fissure segmentation in CT examinations,” IEEE Trans Med Imaging , vol. 28, no. 5, pp. 710–719, 2008
2008
Earlier work this paper cites.
E. M. van Rikxoort, M. Prokop, B. de Hoop, M. A. Viergever, J. P. Pluim, and B. van Ginneken, “Automatic segmentation of the pulmonary lobes from fissures, airways, and lung borders: evaluation of robustness against missing data,” in MICCAI , 2009, pp. 263–271
2009
Earlier work this paper cites.
E. M. Van Rikxoort, M. Prokop, B. de Hoop, M. A. Viergever, J. P. Pluim, and B. van Ginneken, “Automatic segmentation of pulmonary lobes robust against incomplete fissures,” IEEE Trans Med Imaging , vol. 29, no. 6, pp. 1286–1296, 2010
2010
Earlier work this paper cites.
E. A. Regan, J. E. Hokanson, J. R. Murphy, B. Make, D. A. Lynch, T. H. Beaty, D. Curran-Everett, E. K. Silverman, and J. D. Crapo, “Genetic epidemiology of COPD (COPDGene) study design,” COPD , vol. 7, no. 1, pp. 32–43, 2011
2011
Earlier work this paper cites.
B. Lassen, E. M. van Rikxoort, M. Schmidt, S. Kerkstra, B. van Ginneken, and J.-M. Kuhnigk, “Automatic segmentation of the pulmonary lobes from chest CT scans based on fissures, vessels, and bronchi,” IEEE Trans Med Imaging , vol. 32, no. 2, pp. 210–222, 2012
2012
Earlier work this paper cites.
R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” in ICML , 2013, pp. 1310–1318
2013
Earlier work this paper cites.
2014
Cited alongside, same era.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in CVPR , 2014, pp. 580–587
2014
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 ICCV , 2015, pp. 1026–1034
2015
Cited alongside, same era.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3DV . IEEE, 2016, pp. 565–571
2016
Cited alongside, same era.
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: learning dense volumetric segmentation from sparse annotation,” in MICCAI , 2016, pp. 424–432
F. T. Ferreira, P. Sousa, A. Galdran, M. R. Sousa, and A. Campilho, “End-to-end supervised lung lobe segmentation,” in IJCNN , 2018, pp. 1–8
2018
Later among the works it cites.
S. E. Gerard, T. J. Patton, G. E. Christensen, J. E. Bayouth, and J. M. Reinhardt, “Fissurenet: A deep learning approach for pulmonary fissure detection in CT images,” IEEE Trans Med Imaging , vol. 38, no. 1, pp. 156–166, 2018
2018
Later among the works it cites.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in CVPR , 2018, pp. 7794–7803
2018
Later among the works it cites.
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
R. P. Poudel, P. Lamata, and G. Montana, “Recurrent fully convolutional neural networks for multi-slice mri cardiac segmentation,” in Reconstruction, segmentation, and analysis of medical images , 2016, pp. 83–94
2016
Cited alongside, same era.
A. Shrivastava, A. Gupta, and R. Girshick, “Training region-based object detectors with online hard example mining,” in CVPR , 2016, pp. 761–769
2016
Cited alongside, same era.
F. J. Bragman, J. R. McClelland, J. Jacob, J. R. Hurst, and D. J. Hawkes, “Pulmonary lobe segmentation with probabilistic segmentation of the fissures and a groupwise fissure prior,” IEEE Trans Med Imaging , vol. 36, no. 8, pp. 1650–1663, 2017
2017
Cited alongside, same era.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in CVPR , 2017, pp. 4700–4708
2017
Cited alongside, same era.
K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation,” Med Imag Anal , vol. 36, pp. 61–78, 2017
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in NIPS , 2017, pp. 5998–6008
2017
Cited alongside, same era.
C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso, “Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations,” in DLMIA , 2017, pp. 240–248
2017
Cited alongside, same era.
2019
Later among the works it cites.
W. Wang, J. Chen, J. Zhao, Y. Chi, X. Xie, L. Zhang, and X. Hua, “Automated segmentation of pulmonary lobes using coordination-guided deep neural networks,” in ISBI . IEEE, 2019, pp. 1353–1357
2019
Later among the works it cites.
S. E. Gerard and J. M. Reinhardt, “Pulmonary lobe segmentation using a sequence of convolutional neural networks for marginal learning,” in ISBI , 2019, pp. 1207–1211
2019
Later among the works it cites.
H. Hu, Z. Zhang, Z. Xie, and S. Lin, “Local relation networks for image recognition,” in ICCV , 2019, pp. 3464–3473
2019
Later among the works it cites.
2019
Later among the works it cites.
M. Prokop, W. van Everdingen, T. van Rees Vellinga, J. Quarles van Ufford, L. Stöger, L. Beenen, B. Geurts, H. Gietema, J. Krdzalic, C. Schaefer-Prokop, B. van Ginneken, M. Brink, and The “COVID-19 Standardized Reporting” Working Group of the Dutch Radiological Society, “CO-RADS – a categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation,” Radiology , p. 201473, 2020. [Online]. Available: https://pubs.rsna.org/doi/abs/10.1148/radiol.2020201473
2020
Closest in time.
S. E. Gerard, J. Herrmann, D. W. Kaczka, G. Musch, A. Fernandez-Bustamante, and J. M. Reinhardt, “Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species,” Med Imag Anal , vol. 60, p. 101592, 2020
2020
Closest in time.
A. Bernheim, X. Mei, M. Huang, Y. Yang, Z. A. Fayad, N. Zhang, K. Diao, B. Lin, X. Zhu, K. Li, S. Li, H. Shan, A. Jacobi, and M. Chung, “Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection,” Radiology , p. 200463, 2020
2020
Closest in time.
H. Shi, X. Han, N. Jiang, Y. Cao, O. Alwalid, J. Gu, Y. Fan, and C. Zheng, “Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study,” Lancet Infect Dis , 2020
2020
Closest in time.