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Efficient automatic segmentation of multi-level (i.e.
Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the anode09 study
Van Ginneken, B., Armato III, S.G., de Hoop, B., van Amelsvoort-van de Vorst, S., Duindam, T., Niemeijer, M., Murphy, K., Schilham, A., Retico, A., Fantacci, M.E., et al., 2010 · 2010
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Computed tomographic measures of pulmonary vascular morphology in smokers and their clinical implications
Estépar, R.S.J., Kinney, G.L., Black-Shinn, J.L., Bowler, R.P., Kindlmann, G.L., Ross, J.C., Kikinis, R., Han, M.K., Come, C.E., Diaz, A.A., et al., 2013 · 2013
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Automatic pulmonary artery-vein separation and classification in computed tomography using tree partitioning and peripheral vessel matching
Charbonnier, J.P., Brink, M., Ciompi, F., Scholten, E.T., Schaefer-Prokop, C.M., Van Rikxoort, E.M., 2015 · 2015
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2015 esc/ers guidelines for the diagnosis and treatment of pulmonary hypertension: the joint task force for the diagnosis and treatment of pulmonary hypertension of the european society of cardiology (esc) and the european respiratory society (ers): endorsed by: Association for european paediatric and congenital cardiology (aepc), international society for heart and lung transplantation (ishlt)
Galiè, N., Humbert, M., Vachiery, J.L., Gibbs, S., Lang, I., Torbicki, A., Simonneau, G., Peacock, A., Vonk Noordegraaf, A., Beghetti, M., et al., 2016 · 2015
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U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical Image Computing and Computer-assisted Intervention (MICCAI), Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
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3d u-net: learning dense volumetric segmentation from sparse annotation, in: International Conference on Medical Image Computing and Computer-assisted Intervention (MICCAI), Springer. pp. 424–432
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O., 2016 · 2016
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Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
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Focal loss for dense object detection, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P., 2017 · 2017
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Decoupled weight decay regularization, in: International Conference on Learning Representations (ICLR)
Loshchilov, I., Hutter, F., 2018 · 2018
Cited alongside, same era.
Road extraction by deep residual u-net
Zhang, Z., Liu, Q., Wang, Y., 2018 · 2018
Cited alongside, same era.
2019 esc guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the european respiratory society (ers) the task force for the diagnosis and management of acute pulmonary embolism of the european society of cardiology (esc)
Konstantinides, S.V., Meyer, G., Becattini, C., Bueno, H., Geersing, G.J., Harjola, V.P., Huisman, M.V., Humbert, M., Jennings, C.S., Jiménez, D., et al., 2020 · 2019
Cited alongside, same era.
Bias: Transparent reporting of biomedical image analysis challenges
Maier-Hein, L., Reinke, A., Kozubek, M., Martel, A.L., Arbel, T., Eisenmann, M., Hanbury, A., Jannin, P., Müller, H., Onogur, S., et al., 2020 · 2020
Cited alongside, same era.
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge
Comparing methods of detecting and segmenting unruptured intracranial aneurysms on tof-mras: The adam challenge
Timmins, K.M., van der Schaaf, I.C., Bennink, E., Ruigrok, Y.M., An, X., Baumgartner, M., Bourdon, P., De Feo, R., Di Noto, T., Dubost, F., et al., 2021 · 2021
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Methods and open-source toolkit for analyzing and visualizing challenge results
Wiesenfarth, M., Reinke, A., Landman, B.A., Eisenmann, M., Saiz, L.A., Cardoso, M.J., Maier-Hein, L., Kopp-Schneider, A., 2021 · 2021
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Alleviating class-wise gradient imbalance for pulmonary airway segmentation
Zheng, H., Qin, Y., Gu, Y., Xie, F., Yang, J., Sun, J., Yang, G.Z., 2021 · 2021
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Fast and low-gpu-memory abdomen ct organ segmentation: The flare challenge
Ma, J., Zhang, Y., Gu, S., An, X., Wang, Z., Ge, C., Wang, C., Zhang, F., Wang, Y., Xu, Y., et al., 2022 · 2022
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Head and neck tumor segmentation in pet/ct: the hecktor challenge
Oreiller, V., Andrearczyk, V., Jreige, M., Boughdad, S., Elhalawani, H., Castelli, J., Vallières, M., Zhu, S., Xie, J., Peng, Y., et al., 2022 · 2022
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Heller, N., Isensee, F., Maier-Hein, K.H., Hou, X., Xie, C., Li, F., Nan, Y., Mu, G., Lin, Z., Han, M., et al., 2021 · 2021
Cited alongside, same era.
nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H., 2021 · 2021
Cited alongside, same era.
cldice-a novel topology-preserving loss function for tubular structure segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16560–16569
Shit, S., Paetzold, J.C., Sekuboyina, A., Ezhov, I., Unger, A., Zhylka, A., Pluim, J.P., Bauer, U., Menze, B.H., 2021 · 2021
Cited alongside, same era.
Automated vessel segmentation in lung ct and cta images via deep neural networks
Tan, W., Zhou, L., Li, X., Yang, X., Chen, Y., Yang, J., 2021 · 2021
Cited alongside, same era.
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Automated lung vessel segmentation reveals blood vessel volume redistribution in viral pneumonia
Poletti, J., Bach, M., Yang, S., Sexauer, R., Stieltjes, B., Rotzinger, D.C., Bremerich, J., Sauter, A.W., Weikert, T., 2022 · 2022
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Quantitative ct evaluation of small pulmonary vessels has functional and prognostic value in pulmonary hypertension
Shahin, Y., Alabed, S., Alkhanfar, D., Tschirren, J., Rothman, A.M., Condliffe, R., Wild, J.M., Kiely, D.G., Swift, A.J., 2022 · 2022
Later among the works it cites.