LGAN: Lung segmentation in CT scans using generative adversarial network

Adams, 1994, Seeded region growing, TPAMI, 16, 641, 10.1109/34.295913 American Cancer Society, 2016 Aresta, 2019, iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network, Sci. Rep., 9, 1, 10.1038/s41598-019-48004-8 Arjovsky, 2017 Arjovsky, 2017 Armato, 2011, The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans, Med Phys., 38, 915, 10.1118/1.3528204 Baka, 2017, Ultrasound aided vertebral level localization for lumbar surgery, TMI, 36, 2138 Goodfellow, 2014, Generative adversarial nets, 2672 Han, 2015, Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme, JBHI, 19, 648 Huang, 2019, Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks, Comput. Med. Imaging Graph., 74, 25, 10.1016/j.compmedimag.2019.02.003 Jégou, 2017, The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation, 1175 Kallenberg, 2016, Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring, TMI, 35, 1322 Kass, 1988, Snakes: active contour models, IJCV, 1, 321, 10.1007/BF00133570 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Long, 2015, Fully convolutional networks for semantic segmentation, 3431 Maas, 2013, Rectifier nonlinearities improve neural network acoustic models Manivannan, 2017, Structure Prediction for Gland Segmentation with Hand-Crafted and Deep Convolutional Features, TMI Mansoor, 2015, Segmentation and image analysis of abnormal lungs at CT: current approaches, challenges, and future trends, Radiographics, 35, 1056, 10.1148/rg.2015140232 Noh, 2015, Learning deconvolution network for semantic segmentation, 1520 Nutanong, 2011, An incremental Hausdorff distance calculation algorithm, VLDB, 4, 506 P. Luc, C. Couprie, S. Chintala, J. Verbeek, 2016. Semantic segmentation using adversarial networks, arXiv preprint arXiv:1611.08408. R. LaLonde, U. Bagci, 2018, Capsules for Object Segmentation, arXiv preprint arXiv:1804.04241. D. P. Kingma, J. Ba, 2014. Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980. A. P. Harrison, Z. Xu, K. George, L. Lu, R.M. Summers, D.J. Mollura, 2017. Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images, arXiv preprint arXiv:1706.03702. D. Goldgof, et al., 2015. Data From QIN LUNG CT, TCIA. Z. Peng, X. Fang, P. Yan, H. Shan, T. Liu, X. Pei, G. Wang, B. Liu, M.K. Kalra, X.G. Xu, 2020. A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing, Medical Physics. Rockafellar, 2009 Ronneberger, 2015, U-net: convolutional networks for biomedical image segmentation, 234 Shin, 2016, Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, TMI, 35, 1285 Shojaii, 2005, Automatic lung segmentation in CT images using watershed transform Sun, 2012, Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach, TMI, 31, 449 Tan, 2017, Apply convolutional neural network to lung nodule detection: recent progress and challenges, 214 Tan, 2019, LGAN: Lung Segmentation in CT Scans Using Generative Adversarial Network Xue, 2010, Joint registration and segmentation of serial lung CT images for image-guided lung cancer diagnosis and therapy, Comput. Med. Imaging Graph., 34, 55, 10.1016/j.compmedimag.2009.05.007 Yang, 2017, Lung field segmentation in chest radiographs from boundary maps by a structured edge detector, JBHI, 22, 842 Zhao, 2018, Lung segmentation in CT images using a fully convolutional neural network with multi-instance and conditional adversary loss Zhao, 2019, Computerized identification of the vasculature surrounding a pulmonary nodule, Comput. Med. Imaging Graph., 74, 1, 10.1016/j.compmedimag.2019.03.002 A. Radford, L. Metz, S. Chintala, 2015. Unsupervised representation learning with deep convolutional generative adversarial networks, arXiv preprint arXiv:1511.06434.