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

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

2007

Earlier work this paper cites.

Kainmüller, D., Lange, T., Lamecker, H.: Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: Proc. MICCAI Workshop 3D Segmentation in the Clinic: A Grand Challenge. pp. 109–116 (2007)

2007

Earlier work this paper cites.

Heimann, T., Van Ginneken, B., Styner, M.A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., et al.: Comparison and evaluation of methods for liver segmentation from ct datasets. IEEE Transactions on Medical Imaging 28(8), 1251–1265 (2009)

2009

Earlier work this paper cites.

Wimmer, A., Soza, G., Hornegger, J.: A generic probabilistic active shape model for organ segmentation. In: Yang, G., Hawkes, D., Rueckert, D., Nobel, A., Taylor, C. (eds.) MICCAI 2009, PartII. LNCS, vol. 5762, pp. 26-33. Springer, Heidelberg (2009)

2009

Earlier work this paper cites.

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

2010

Earlier work this paper cites.

Krähenbühl, P., Koltun, V.: Efficient inference in fully connected crfs with gaussian edge potentials. arXiv preprint arXiv:1210.5644 (2012)

Original

2012

Cited alongside, same era.

Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. arXiv preprint arXiv:1409.5185 (2014)

Original

2014

Cited alongside, same era.

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)

Original

2015

Cited alongside, same era.

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE CVPR. pp. 3431–3440 (2015)

2015

Cited alongside, same era.

Wolterink, J.M., Leiner, T., Viergever, M.A., Išgum, I.: Automatic coronary calcium scoring in cardiac ct angiography using convolutional neural networks. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015, PartI. LNCS, vol. 9349, pp. 589-596. Springer, Switzerland (2015)

Original

2015

Later among the works it cites.

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

Original

2016

Closest in time.

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)

2016

Closest in time.

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. arXiv preprint arXiv:1603.05959 (2016)

Original

2016

Closest in time.