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We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process.
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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 . Springer, 2016, pp. 424–432
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2019
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M. C. H. Lee, K. Petersen, N. Pawlowski, B. Glocker, and M. Schaap, “TETRIS: Template transformer networks for image segmentation with shape priors,” IEEE transactions on medical imaging , 2019
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Closest in time.
2019
Closest in time.
2019
Closest in time.
X. Hu, L. Fuxin, D. Samaras, and C. Chen, “Topology-preserving deep image segmentation,” in 33rd Conference on Neural Information Processing Systems (NeurIPS) , 2019
2019
Closest in time.