On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks

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Ç Özgün, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U–Net: Learning Dense Volumetric Segmentation from Sparse Annotation,” MICCAI 2016, Part II, LNCS 9901, pp.424–432, 2016

2016

Cited alongside, same era.

F. Milletari, N. Navab, and S. A. Ahmadi, “V–net: Fully convolutional neural networks for volumetric medical image segmentation,” 3D Vision (3DV), 2016 Fourth International Conference on. IEEE, 2016

2016

Cited alongside, same era.

H. R. Roth, M. Oda, Y. Hayashi, H. Oda, and K. Mori, “Multi–organ segmentation in abdominal CT using 3D fully convolutional networks,” International Journal of Computer Assisted Radiology and Surgery, Vol.12, Sup.1, pp.S55–S57, 2017

2017

Cited alongside, same era.

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Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., and Cardoso, M. J. “Generalized 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, pp.240–248, 2017

2017

Later among the works it cites.

A. A. Novikov, D. Lenis, D. Major, J. Hladuvka, M. Wimmer, and K. Bühler, “Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs,” Computing Research Repository, vol.abs/1701.08816, 2017

Original

2017

Later among the works it cites.

H. R. 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,” SPIE. Medical Imaging, 2018.(accepted)

2018

Closest in time.