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The Active Contour Model (ACM) is a standard image analysis technique whose numerous variants have attracted an enormous amount of research attention across multiple fields.
Snakes: Active contour models
M. Kass, A. Witkin, and D. Terzopoulos · 1988
Earlier work this paper cites.
Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations
S. Osher and J. A. Sethian · 1988
Earlier work this paper cites.
Geodesic active contours
V. Caselles, R. Kimmel, and G. Sapiro · 1997
Earlier work this paper cites.
Active contours without edges
T. F. Chan and L. A. Vese · 2001
Earlier work this paper cites.
Level set methods: An overview and some recent results
S. Osher and R. P. Fedkiw · 2001
Earlier work this paper cites.
Localizing region-based active contours
S. Lankton and A. Tannenbaum · 2008
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Effective semantic pixel labelling with convolutional networks and conditional random fields
S. Paisitkriangkrai, J. Sherrah, P. Janney, V.-D. Hengel, et al · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al · 2016
Cited alongside, same era.
A benchmark dataset and evaluation methodology for video object segmentation
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung · 2016
Cited alongside, same era.
Torontocity: Seeing the world with a million eyes
S. Wang, M. Bai, G. Mattyus, H. Chu, W. Luo, B. Yang, J. Liang, J. Cheverie, S. Fidler, and R. Urtasun · 2016
Cited alongside, same era.
Rethinking atrous convolution for semantic image segmentation
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam · 2017
Cited alongside, same era.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
Ternausnetv2: Fully convolutional network for instance segmentation
V. Iglovikov, S. Seferbekov, A. Buslaev, and A. Shvets · 2018
Later among the works it cites.
Automatic segmentation of pulmonary lobes using a progressive dense V-network
A.-A.-Z. Imran, A. Hatamizadeh, S. P. Ananth, X. Ding, D. Terzopoulos, and N. Tajbakhsh · 2018
Later among the works it cites.
Reformulating level sets as deep recurrent neural network approach to semantic segmentation
T. H. N. Le, K. G. Quach, K. Luu, C. N. Duong, and M. Savvides · 2018
Later among the works it cites.
Learning deep structured active contours end-to-end
D. Marcos, D. Tuia, B. Kellenberger, L. Zhang, M. Bai, R. Liao, and R. Urtasun · 2018
Later among the works it cites.
Deep active lesion segmentation
A. Hatamizadeh, A. Hoogi, D. Sengupta, W. Lu, B. Wilcox, D. Rubin, and D. Terzopoulos · 2019
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C. Xie, J. Wang, Z. Zhang, Z. Ren, and A. Yuille · 2017
Cited alongside, same era.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
Cited alongside, same era.
A. Hatamizadeh, H. Hosseini, Z. Liu, S. D. Schwartz, and D. Terzopoulos · 2019
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End-to-end boundary aware networks for medical image segmentation
A. Hatamizadeh, D. Terzopoulos, and A. Myronenko · 2019
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3d kidneys and kidney tumor semantic segmentation using boundary-aware networks
A. Myronenko and A. Hatamizadeh · 2019
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