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Like other applications in computer vision, medical image segmentation has been most successfully addressed using deep learning models that rely on the convolution operation as their main building block.
Le Cun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Handwritten digit recognition with a back-propagation network. In: Proceedings of the 2nd International Conference on Neural Information Processing Systems. pp. 396–404 (1989)
1989
Earlier work this paper cites.
Prince, J.L., Pham, D., Tan, Q.: Optimization of mr pulse sequences for bayesian image segmentation. Medical Physics 22
1995
Earlier work this paper cites.
Gibbs, P., Buckley, D.L., Blackband, S.J., Horsman, A.: Tumour volume determination from mr images by morphological segmentation. Physics in Medicine & Biology 41
1996
Earlier work this paper cites.
Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381
1996
Earlier work this paper cites.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9
1997
Earlier work this paper cites.
Thompson, P.M., Toga, A.W.: Detection, visualization and animation of abnormal anatomic structure with a deformable probabilistic brain atlas based on random vector field transformations. Medical image analysis 1
1997
Earlier work this paper cites.
Goldszal, A.F., Davatzikos, C., Pham, D.L., Yan, M.X., Bryan, R.N., Resnick, S.M.: An image-processing system for qualitative and quantitative volumetric analysis of brain images. Journal of computer assisted tomography 22
1998
Earlier work this paper cites.
Wang, Y., Guo, Q., Zhu, Y.: Medical image segmentation based on deformable models and its applications. In: Deformable Models, pp. 209–260. Springer (2007)
2007
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097–1105 (2012)
2012
Earlier work this paper cites.
Mahapatra, D., Buhmann, J.M.: Prostate mri segmentation using learned semantic knowledge and graph cuts. IEEE Transactions on Biomedical Engineering 61
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR) (2014)
2014
Earlier work this paper cites.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. nature 521
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. pp. 91–99 (2015)
2015
Cited alongside, same era.
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep learning, vol. 1. MIT press Cambridge (2016)
2016
Cited alongside, same era.
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 3D Vision (3DV), 2016 Fourth International Conference on. pp. 565–571. IEEE (2016)
2016
Cited alongside, same era.
Bai, W., Oktay, O., Sinclair, M., Suzuki, H., Rajchl, M., Tarroni, G., Glocker, B., King, A., Matthews, P.M., Rueckert, D.: Semi-supervised learning for network-based cardiac mr image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 253–260. Springer (2017)
2017
Cited alongside, same era.
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp. 3–11. Springer (2018)
2018
Later among the works it cites.
Bastiani, M., et al.: Automated processing pipeline for neonatal diffusion mri in the developing human connectome project. NeuroImage 185
2019
Later among the works it cites.
Hesamian, M.H., Jia, W., He, X., Kennedy, P.: Deep learning techniques for medical image segmentation: achievements and challenges. Journal of digital imaging 32
2019
Later among the works it cites.
2019
Later among the works it cites.
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Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical image analysis 36
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Bai, W., Suzuki, H., Qin, C., Tarroni, G., Oktay, O., Matthews, P.M., Rueckert, D.: Recurrent neural networks for aortic image sequence segmentation with sparse annotations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 586–594. Springer (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Ballester, M.A.G., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging 37
2018
Cited alongside, same era.
Gao, Y., Phillips, J.M., Zheng, Y., Min, R., Fletcher, P.T., Gerig, G.: Fully convolutional structured lstm networks for joint 4d medical image segmentation. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). pp. 1104–1108 (2018). https://doi.org/10.1109/ISBI.2018.8363764
2018
Cited alongside, same era.
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. In: International MICCAI Brainlesion Workshop. pp. 234–244. Springer (2018)
2018
Cited alongside, same era.
Karimi, D., Samei, G., Kesch, C., Nir, G., Salcudean, S.E.: Prostate segmentation in mri using a convolutional neural network architecture and training strategy based on statistical shape models. International Journal of Computer Assisted Radiology and Surgery 13
2018
Cited alongside, same era.
Karimi, D., et al.: Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Medical Image Analysis 57
2019
Later among the works it cites.
Zeng, Q., Karimi, D., Pang, E.H., Mohammed, S., Schneider, C., Honarvar, M., Salcudean, S.E.: Liver segmentation in magnetic resonance imaging via mean shape fitting with fully convolutional neural networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 246–254. Springer (2019)
2019
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2020
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2020
Later among the works it cites.
Taghanaki, S.A., Abhishek, K., Cohen, J.P., Cohen-Adad, J., Hamarneh, G.: Deep semantic segmentation of natural and medical images: A review. Artificial Intelligence Review pp. 1–42 (2020)
2020
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2020
Later among the works it cites.
Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., Chen, L.C.: Axial-deeplab: Stand-alone axial-attention for panoptic segmentation. In: European Conference on Computer Vision. pp. 108–126. Springer (2020)
2020
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2021
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