3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

Author:

Dou Qi,Chen Hao,Jin Yueming,Yu Lequan,Qin Jing,Heng Pheng-Ann

Publisher

Springer International Publishing

Reference13 articles.

1. Al-Shaikhli, S.D.S., Yang, M.Y., Rosenhahn, B.: Automatic 3D liver segmentation using sparse representation of global and local image information via level set formulation. arXiv preprint arXiv:1508.01521 (2015)

2. Chen, H., Qi, X., Yu, L., Heng, P.A.: Dcan: deep contour-aware networks for accurate gland segmentation. arXiv preprint arXiv:1604.02677 (2016)

3. Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V.C., Shi, L., Heng, P.A.: Automatic detection of cerebral microbleeds from mr images via 3D convolutional neural networks. IEEE TMI 35(5), 1182–1195 (2016)

4. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS, pp. 249–256 (2010)

5. Heimann, T., Meinzer, H.P., Wolf, I.: A statistical deformable model for the segmentation of liver CT volumes. In: Proceedings of MICCAI Workshop. 3D Segmentation in the Clinic: A Grand Challenge, pp. 161–166 (2007)

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