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This paper deals with segmentation of organs at risk (OAR) in head and neck area in CT images which is a crucial step for reliable intensity modulated radiotherapy treatment.
Dice, L. R.: Measures of the Amount of Ecologic Association Between Species. Ecology, 26(3):297-302 (1945)
1945
Earlier work this paper cites.
Dawson, L. A., Sharpe, M. B.: Image-guided radiotherapy: rationale, benefits, and limitations. Lancet Oncology, 7(10), 848–858 (2006)
2006
Earlier work this paper cites.
Van Ginneken, B., Heimann, T., Styner, M.: 3D segmentation in the clinic: A grand challenge. MICCAI Workshop on 3D Segmentation in the Clinic: A Grand Challenge. (2007)
2007
Earlier work this paper cites.
Heimann T, Meinzer HP. Statistical shape models for 3D medical image segmentation: a review. Med. Image Analy, 13:543–563 (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advance in Neural Information Processing Systems, pp. 1097–1105 (2012)
2012
Earlier work this paper cites.
Pastor-Pellicer, J., Zamora-Martínez, F., España-Boquera, S., Castro-Bleda, M. J.: International Work-Conference on Artificial Neural Networks, 376-384 (2013)
2013
Earlier work this paper cites.
Jung F, Steger S, Knapp O, Noll M, Wesarg S. COSMO - coupled shape model for radiation therapy planning of head and neck cancer. Clinical Image-Based Procedures, LNCS, vol. 8680. Cham: Springer; 25–32 (2014)
2014
Cited alongside, same era.
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham (2015)
2015
Cited alongside, same era.
Fritscher, K., Raudaschl, P., Zaffino, P., Spadea, M. F., Sharp, G. C., Schubert, R.: Deep Neural Networks for Fast Segmentation of 3D Medical Images. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Lecture Notes in Computer Science, vol 9901. Springer, Cham (2016)
2016
Cited alongside, same era.
Milletari, F., Navab, N., Ahmadi, S.: V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Fourth International Conference on 3D Vision (3DV), Stanford, CA, 2016, pp. 565-571. (2016)
Ibragimov, B., Xing, L.: Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Medical Physics, 44(2), 547–557. (2017)
2017
Later among the works it cites.
Sudre, C. H., Li, W., Vecauteren, T., Ourselin, S., Cardoso, M. J.: Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA 2017, ML-CDS 2017. Lecture Notes in Computer Science, vol 10553. Springer, Cham (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
Wang, Z., Wei, L., Wang, L., Gao, Y., Chen, W., Shen, D.: Hierarchical Vertex Regression-Based Segmentation of Head and Neck CT Images for Radiotherapy Planning. IEEE Transactions on Image Processing, 27(2), 923–937. (2018)
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2016
Cited alongside, same era.
Raudaschl, P. F., Zaffino, P., Sharp, G. C., Spadea, M. F., Chen, A., Dawant, B. M., Fritscher, K. D.: Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015. Medical Physics, 44(5), 2020–2036 (2017)
2017
Cited alongside, same era.
2018
Closest in time.
Fidon, L., Li, W., Garcia-Peraza-Herrera, L. C., Ekanayake, J., Kitchen, N., Ourselin, S., Vercauteren, T.: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science, vol 10670. Springer, Cham (2018)
2018
Closest in time.