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The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation methods.
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2013
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N. Makni, N. Betrouni, and O. Colot, “Introducing spatial neighbourhood in evidential c-means for segmentation of multi-source images: Application to prostate multi-parametric mri,” Information Fusion , vol. 19, pp. 61–72, 2014
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G. Litjens, R. Toth, W. van de Ven, C. Hoeks, S. Kerkstra, B. van Ginneken, G. Vincent, G. Guillard, N. Birbeck, J. Zhang, R. Strand, F. Malmberg, Y. Ou, C. Davatzikos, M. Kirschner, F. Jung, J. Yuan, W. Qiu, Q. Gao, P. Edwards, B. Maan, F. van der Heijden, S. Ghose, J. Mitra, J. Dowling, D. Barratt, H. Huisman, and A. Madabhushi, “Evaluation of prostate segmentation algorithms for mri: The promise12 challenge,” Medical Image Analysis , vol. 18, no. 2, pp. 359 – 373, 2014
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2015
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2017
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2017
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L. Yu, X. Yang, H. Chen, J. Qin, and P. A. Heng, “Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d MR images,” in Thirty-first AAAI conference on artificial intelligence , 2017
2017
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2018
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2018
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2018
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2018
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H. Roth, M. Oda, N. Shimizu, H. Oda, Y. Hayashi, T. Kitasaka, M. Fujiwara, K. Misawa, and K. Mori, “Towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks,” in Medical Imaging 2018: Image Processing , vol. 10574. International Society for Optics and Photonics, 2018, p. 105740B
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L. Wang, D. Nie, G. Li, É. Puybareau, J. Dolz, Q. Zhang, F. Wang, J. Xia, Z. Wu, J. Chen et al. , “Benchmark on automatic 6-month-old infant brain segmentation algorithms: The iseg-2017 challenge,” IEEE Transactions on Medical Imaging , 2019
2019
Closest in time.
S. R. Hashemi, S. S. M. Salehi, D. Erdogmus, S. P. Prabhu, S. K. Warfield, and A. Gholipour, “Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: Application to multiple sclerosis lesion detection,” IEEE Access , vol. 7, pp. 1721–1735, 2019
2019
Closest in time.