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With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets.
M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” International Journal of Computer Vision , vol. 1, no. 4, pp. 321–331, 1988
1988
Earlier work this paper cites.
M. McCloskey and N. J. Cohen, “Catastrophic interference in connectionist networks: The sequential learning problem,” in Psychology of Learning and Motivation , 1989, vol. 24, pp. 109–165
1989
Earlier work this paper cites.
T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active shape models-their training and application,” Computer vision and image understanding , vol. 61, no. 1, pp. 38–59, 1995
1995
Earlier work this paper cites.
H.-P. Meinzer, M. Thorn, and C. E. Cárdenas, “Computerized planning of liver surgery—an overview,” Computers & Graphics , vol. 26, no. 4, pp. 569–576, 2002
2002
Earlier work this paper cites.
X. Zhou, T. Kitagawa, K. Okuo, T. Hara, H. Fujita, R. Yokoyama, M. Kanematsu, and H. Hoshi, “Construction of a probabilistic atlas for automated liver segmentation in non-contrast torso ct images,” in International Congress Series , vol. 1281, 2005, pp. 1169–1174
2005
Earlier work this paper cites.
T. Heimann, B. Van Ginneken, M. A. Styner, Y. Arzhaeva, V. Aurich, C. Bauer, A. Beck, C. Becker, R. Beichel, G. Bekes et al. , “Comparison and evaluation of methods for liver segmentation from ct datasets,” IEEE Transactions on Medical Imaging , vol. 28, no. 8, pp. 1251–1265, 2009
2009
Earlier work this paper cites.
X. Zhang, J. Tian, K. Deng, Y. Wu, and X. Li, “Automatic liver segmentation using a statistical shape model with optimal surface detection,” IEEE Transactions on Biomedical Engineering , vol. 57, no. 10, pp. 2622–2626, 2010
2010
Earlier work this paper cites.
B. Van Ginneken, C. M. Schaefer-Prokop, and M. Prokop, “Computer-aided diagnosis: how to move from the laboratory to the clinic,” Radiology , vol. 261, no. 3, pp. 719–732, 2011
2011
Earlier work this paper cites.
P. Krähenbühl and V. Koltun, “Efficient inference in fully connected crfs with gaussian edge potentials,” in Advances in Neural Information Processing Systems , vol. 24, 2011, pp. 109–117
2011
Earlier work this paper cites.
K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle et al. , “The cancer imaging archive (tcia): maintaining and operating a public information repository,” Journal of Digital Imaging , vol. 26, no. 6, pp. 1045–1057, 2013
2013
Earlier work this paper cites.
D.-H. Lee, “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,” in Workshop on Challenges in Representation Learning, ICML , vol. 3, 2013, p. 2
2013
Earlier work this paper cites.
J. Sykes, “Reflections on the current status of commercial automated segmentation systems in clinical practice,” Journal of Medical Radiation Sciences , vol. 61, no. 3, pp. 131–134, 2014
2014
Earlier work this paper cites.
I. J. Goodfellow, M. Mirza, D. Xiao, A. Courville, and Y. Bengio, “An empirical investigation of catastrophic forgeting in gradientbased neural networks,” in In Proceedings of International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
H. R. Roth, L. Lu, A. Farag, H.-C. Shin, J. Liu, E. B. Turkbey, and R. M. Summers, “Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention , 2015, pp. 556–564
2015
Earlier work this paper cites.
J. E. Iglesias and M. R. Sabuncu, “Multi-atlas segmentation of biomedical images: a survey,” Medical Image Analysis , vol. 24, no. 1, pp. 205–219, 2015
2015
Earlier work this paper cites.
G. Li, X. Chen, F. Shi, W. Zhu, J. Tian, and D. Xiang, “Automatic liver segmentation based on shape constraints and deformable graph cut in ct images,” IEEE Transactions on Image Processing , vol. 24, no. 12, pp. 5315–5329, 2015
2015
Earlier work this paper cites.
J. Peng, P. Hu, F. Lu, Z. Peng, D. Kong, and H. Zhang, “3d liver segmentation using multiple region appearances and graph cuts,” Medical Physics , vol. 42, no. 12, pp. 6840–6852, 2015
2015
Earlier work this paper cites.
T. Okada, M. G. Linguraru, M. Hori, R. M. Summers, N. Tomiyama, and Y. Sato, “Abdominal multi-organ segmentation from ct images using conditional shape–location and unsupervised intensity priors,” Medical Image Analysis , vol. 26, no. 1, pp. 1–18, 2015
2015
Earlier work this paper cites.
Z. Xu, R. P. Burke, C. P. Lee, R. B. Baucom, B. K. Poulose, R. G. Abramson, and B. A. Landman, “Efficient multi-atlas abdominal segmentation on clinically acquired ct with simple context learning,” Medical Image Analysis , vol. 24, no. 1, pp. 18–27, 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention , 2015, pp. 234–241
2015
Earlier work this paper cites.
D. Pathak, P. Krahenbuhl, and T. Darrell, “Constrained convolutional neural networks for weakly supervised segmentation,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1796–1804
2015
Earlier work this paper cites.
B. Landman, Z. Xu, J. Igelsias, M. Styner, T. Langerak, and A. Klein, “Multi-atlas labeling beyond the cranial vault-workshop and challenge,” 2015
2015
Earlier work this paper cites.
H. R. Roth, A. Farag, E. B. Turkbey, L. Lu, J. Liu, and R. M. Summers, “Data from pancreas-ct,” The Cancer Imaging Archive, 2016
2016
Earlier work this paper cites.
H. R. Roth, L. Lu, A. Farag, A. Sohn, and R. M. Summers, “Spatial aggregation of holistically-nested networks for automated pancreas segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention , 2016, pp. 451–459
2016
Earlier work this paper cites.
X. Zhou, T. Ito, R. Takayama, S. Wang, T. Hara, and H. Fujita, “Three-dimensional ct image segmentation by combining 2d fully convolutional network with 3d majority voting,” in Deep Learning and Data Labeling for Medical Applications , 2016, pp. 111–120
2016
Earlier work this paper cites.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in Fourth International Conference on 3D vision , 2016, pp. 565–571
2016
Earlier work this paper cites.
A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-Fei, “What’s the point: Semantic segmentation with point supervision,” in European Conference on Computer Vision , 2016, pp. 549–565
2016
Earlier work this paper cites.
D. Lin, J. Dai, J. Jia, K. He, and J. Sun, “Scribblesup: Scribble-supervised convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 3159–3167
2016
Earlier work this paper cites.
O. Jimenez-del Toro, H. Müller, M. Krenn, K. Gruenberg, A. A. Taha, M. Winterstein, I. Eggel, A. Foncubierta-Rodríguez, O. Goksel, A. Jakab et al. , “Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: Visceral anatomy benchmarks,” IEEE Transactions on Medical Imaging , vol. 35, no. 11, pp. 2459–2475, 2016
2016
Earlier work this paper cites.
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer-assisted Intervention , 2016, pp. 424–432
2016
Earlier work this paper cites.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D vision , 2016, pp. 565–571
2016
Earlier work this paper cites.
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, “A benchmark dataset and evaluation methodology for video object segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 724–732
2016
Earlier work this paper cites.
M. Gao, Z. Xu, L. Lu, A. Wu, I. Nogues, R. M. Summers, and D. J. Mollura, “Segmentation label propagation using deep convolutional neural networks and dense conditional random field,” in 2016 IEEE 13th International Symposium on Biomedical Imaging , 2016, pp. 1265–1268
2016
Earlier work this paper cites.
P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi et al. , “Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields,” in International Conference on Medical Image Computing and Computer-Assisted Intervention , 2016, pp. 415–423
2016
Earlier work this paper cites.
S. K. Siri and M. V. Latte, “Combined endeavor of neutrosophic set and chan-vese model to extract accurate liver image from ct scan,” Computer Methods and Programs in Biomedicine , vol. 151, pp. 101–109, 2017
2017
Earlier work this paper cites.
K. George, A. P. Harrison, D. Jin, Z. Xu, and D. J. Mollura, “Pathological pulmonary lobe segmentation from ct images using progressive holistically nested neural networks and random walker,” in Deep learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support , 2017, pp. 195–203
2017
Earlier work this paper cites.
A. P. Harrison, Z. Xu, K. George, L. Lu, R. M. Summers, and D. J. Mollura, “Progressive and multi-path holistically nested neural networks for pathological lung segmentation from ct images,” in International Conference on Medical Image Computing and Computer Assisted Intervention , 2017, pp. 621–629
2017
Cited alongside, same era.
Y. Zhou, L. Xie, W. Shen, Y. Wang, E. K. Fishman, and A. L. Yuille, “A fixed-point model for pancreas segmentation in abdominal ct scans,” in International Conference on Medical Image Computing and Computer-assisted Intervention , 2017, pp. 693–701
2017
Cited alongside, same era.
P. Hu, F. Wu, J. Peng, Y. Bao, F. Chen, and D. Kong, “Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets,” International Journal of Computer Assisted Radiology and Surgery , vol. 12, no. 3, pp. 399–411, 2017
2017
Cited alongside, same era.
R. Qian, Y. Wei, H. Shi, J. Li, J. Liu, and T. Huang, “Weakly supervised scene parsing with point-based distance metric learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 8843–8850
2019
Later among the works it cites.
Z. Ji, Y. Shen, C. Ma, and M. Gao, “Scribble-based hierarchical weakly supervised learning for brain tumor segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention , 2019, pp. 175–183
2019
Later among the works it cites.
B. Pfülb and A. Gepperth, “A comprehensive, application-oriented study of catastrophic forgetting in dnns,” in In Proceedings of International Conference on Learning Representations , 2019
2019
Later among the works it cites.
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter, “Continual lifelong learning with neural networks: A review,” Neural Networks , vol. 113, pp. 54–71, 2019
2019
Later among the works it cites.
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2017
Cited alongside, same era.
V. Lomonaco and D. Maltoni, “Core50: a new dataset and benchmark for continuous object recognition,” in Proceedings of the First Annual Conference on Robot Learning , vol. 78, 2017, pp. 17–26
2017
Cited alongside, same era.
R. Camoriano, G. Pasquale, C. Ciliberto, L. Natale, L. Rosasco, and G. Metta, “Incremental robot learning of new objects with fixed update time,” in 2017 IEEE International Conference on Robotics and Automation , 2017, pp. 3207–3214
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Y. Zhang, Z. He, C. Zhong, Y. Zhang, and Z. Shi, “Fully convolutional neural network with post-processing methods for automatic liver segmentation from ct,” in 2017 Chinese Automation Congress , 2017, pp. 3864–3869
2017
Cited alongside, same era.
Z. Li and D. Hoiem, “Learning without forgetting,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 12, pp. 2935–2947, 2017
2017
Cited alongside, same era.
2018
Cited alongside, same era.
O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, X. Yang, P.-A. Heng, I. Cetin, K. Lekadir, O. Camara, M. A. G. Ballester et al. , “Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?” IEEE Transactions on Medical Imaging , vol. 37, no. 11, pp. 2514–2525, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
L. Xie, Q. Yu, Y. Zhou, Y. Wang, E. K. Fishman, and A. L. Yuille, “Recurrent saliency transformation network for tiny target segmentation in abdominal ct scans,” IEEE Transactions on Medical Imaging , vol. 39, no. 2, pp. 514–525, 2019
2019
Later among the works it cites.
2020
Closest in time.
G. E. Humpire-Mamani, J. Bukala, E. T. Scholten, M. Prokop, B. van Ginneken, and C. Jacobs, “Fully automatic volume measurement of the spleen at ct using deep learning,” Radiology: Artificial Intelligence , vol. 2, no. 4, p. e190102, 2020
2020
Closest in time.
J. Mongan, L. Moy, and C. E. Kahn, “Checklist for artificial intelligence in medical imaging (claim): A guide for authors and reviewers,” Radiology: Artificial Intelligence , vol. 2, no. 2, p. e200029, 2020
2020
Closest in time.
B. Norgeot, G. Quer, B. K. Beaulieu-Jones, A. Torkamani, R. Dias, M. Gianfrancesco, R. Arnaout, I. S. Kohane, S. Saria, E. Topol et al. , “Minimum information about clinical artificial intelligence modeling: the mi-claim checklist,” Nature Medicine , vol. 26, no. 9, pp. 1320–1324, 2020
2020
Closest in time.
N. Tajbakhsh, L. Jeyaseelan, Q. Li, J. N. Chiang, Z. Wu, and X. Ding, “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation,” Medical Image Analysis , p. 101693, 2020
2020
Closest in time.
“DAVIS: Densely Annotated VIdeo Segmentation,” https://davischallenge.org/ , 2020, [Online; Accessed: Aug. 2020]
2020
Closest in time.
Z.-K. Ni, D. Lin, Z.-Q. Wang, H.-M. Jin, X.-W. Li, Y. Li, and H. Huang, “Precision liver resection: Three-dimensional reconstruction combined with fluorescence laparoscopic imaging,” Surgical Innovation , 2020
2020
Closest in time.
M. Jun, H. Jian, and Y. Xiaoping, “Learning geodesic active contours for embedding object global information in segmentation cnns,” IEEE Transactions on Medical Imaging , 2020
2020
Closest in time.
D. Guo, D. Jin, Z. Zhu, T.-Y. Ho, A. P. Harrison, C.-H. Chao, J. Xiao, and L. Lu, “Organ at risk segmentation for head and neck cancer using stratified learning and neural architecture search,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4223–4232
2020
Closest in time.
L. Zhang, J. Zhang, P. Shen, G. Zhu, P. Li, X. Lu, H. Zhang, S. A. Shah, and M. Bennamoun, “Block level skip connections across cascaded v-net for multi-organ segmentation,” IEEE Transactions on Medical Imaging , 2020
2020
Closest in time.
J. E. Van Engelen and H. H. Hoos, “A survey on semi-supervised learning,” Machine Learning , vol. 109, no. 2, pp. 373–440, 2020
2020
Closest in time.
H. H. Lee, Y. Tang, O. Tang, Y. Xu, Y. Chen, D. Gao, S. Han, R. Gao, M. R. Savona, R. G. Abramson et al. , “Semi-supervised multi-organ segmentation through quality assurance supervision,” in Medical Imaging 2020: Image Processing , vol. 11313, 2020, p. 113131I
2020
Closest in time.
A. Raju, C.-T. Cheng, Y. Huo, J. Cai, J. Huang, J. Xiao, L. Lu, C. Liao, and A. P. Harrison, “Co-heterogeneous and adaptive segmentation from multi-source and multi-phase ct imaging data: a study on pathological liver and lesion segmentation,” in European Conference on Computer Vision , 2020, pp. 448–465
2020
Closest in time.
Y. Xia, D. Yang, Z. Yu, F. Liu, J. Cai, L. Yu, Z. Zhu, D. Xu, A. Yuille, and H. Roth, “Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation,” Medical Image Analysis , vol. 65, p. 101766, 2020
2020
Closest in time.
Y. Liu, Y.-H. Wu, P.-S. Wen, Y.-J. Shi, Y. Qiu, and M.-M. Cheng, “Leveraging instance-, image-and dataset-level information for weakly supervised instance segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
Closest in time.
X. Wang, S. Liu, H. Ma, and M.-H. Yang, “Weakly-supervised semantic segmentation by iterative affinity learning,” International Journal of Computer Vision , vol. 128, pp. 1736–1749, 2020
2020
Closest in time.
2020
Closest in time.
N. Heller, S. McSweeney, M. T. Peterson, S. Peterson, J. Rickman, B. Stai, R. Tejpaul, M. Oestreich, P. Blake, J. Rosenberg et al. , “An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in ct imaging.” American Society of Clinical Oncology , vol. 38, no. 6, pp. 626–626, 2020
2020
Closest in time.
B. Rister, D. Yi, K. Shivakumar, T. Nobashi, and D. L. Rubin, “Ct-org, a new dataset for multiple organ segmentation in computed tomography,” Scientific Data , vol. 7, no. 1, pp. 1–9, 2020
2020
Closest in time.
Q. Xie, M.-T. Luong, E. Hovy, and Q. V. Le, “Self-training with noisy student improves imagenet classification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 687–10 698
2020
Closest in time.
L.-C. Chen, R. G. Lopes, B. Cheng, M. D. Collins, E. D. Cubuk, B. Zoph, H. Adam, and J. Shlens, “Semi-supervised learning in video sequences for urban scene segmentation,” European Conference on Computer Vision , 2020
2020
Closest in time.
G. Shi, L. Xiao, Y. Chen, and S. K. Zhou, “Marginal loss and exclusion loss for partially supervised multi-organ segmentation,” Medical Image Analysis , vol. 70, p. 101979, 2021
2021
Closest in time.
N. Heller, F. Isensee, K. H. Maier-Hein, X. Hou, C. Xie, F. Li, Y. Nan, G. Mu, Z. Lin, M. Han et al. , “The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge,” Medical Image Analysis , vol. 67, p. 101821, 2021
2021
Closest in time.
F. Isensee, P. F. Jäeger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature Methods , vol. 18, no. 2, pp. 203–211, 2021
2021
Closest in time.
D. Jin, D. Guo, T.-Y. Ho, A. P. Harrison, J. Xiao, C.-k. Tseng, and L. Lu, “Deeptarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy,” Medical Image Analysis , vol. 68, p. 101909, 2021
2021
Closest in time.
Y. He, D. Yang, H. Roth, C. Zhao, and D. Xu, “Dints: Differentiable neural network topology search for 3d medical image segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2021
2021
Closest in time.
2021
Closest in time.
J. Ma, J. Chen, M. Ng, R. Huang, Y. Li, C. Li, X. Yang, and A. Martel, “Loss odyssey in medical image segmentation,” Medical Image Analysis , vol. 71, p. 102035, 2021
2021
Closest in time.