V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Roehrborn, C.G., Boyle, P., Bergner, D., Gray, T., Gittelman, M., Shown, T., Melman, A., Bracken, R.B., deVere White, R., Taylor, A., et al.: Serum prostate-specific antigen and prostate volume predict long-term changes in symptoms and flow rate: results of a four-year, randomized trial comparing finasteride versus placebo. Urology 54(4), 662–669 (1999)

1999

Earlier work this paper cites.

Porter, C.R., Crawford, E.D.: Combining artificial neural networks and transrectal ultrasound in the diagnosis of prostate cancer. Oncology (Williston Park, NY) 17(10), 1395–9 (2003)

2003

Earlier work this paper cites.

Huyskens, D.P., Maingon, P., Vanuytsel, L., Remouchamps, V., Roques, T., Dubray, B., Haas, B., Kunz, P., Coradi, T., Bühlman, R., et al.: A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer. Radiotherapy and Oncology 90(3), 337–345 (2009)

2009

Earlier work this paper cites.

Moradi, M., Mousavi, P., Boag, A.H., Sauerbrei, E.E., Siemens, D.R., Abolmaesumi, P.: Augmenting detection of prostate cancer in transrectal ultrasound images using svm and rf time series. Biomedical Engineering, IEEE Transactions on 56(9), 2214–2224 (2009)

2009

Earlier work this paper cites.

Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4itk: improved n3 bias correction. Medical Imaging, IEEE Transactions on 29(6), 1310–1320 (2010)

2010

Earlier work this paper cites.

Vincent, G., Guillard, G., Bowes, M.: Fully automatic segmentation of the prostate using active appearance models. MICCAI Grand Challenge PROMISE 2012 (2012)

2012

Earlier work this paper cites.

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)

Original

2014

Earlier work this paper cites.

Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al.: Evaluation of prostate segmentation algorithms for mri: the promise12 challenge. Medical image analysis 18(2), 359–373 (2014)

2014

Cited alongside, same era.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

Original

2014

Cited alongside, same era.

Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806 (2014)

Original

2014

Cited alongside, same era.

Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Computer vision–ECCV 2014, pp. 818–833. Springer (2014)

2014

Cited alongside, same era.

Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1520–1528 (2015)

2015

Later among the works it cites.

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015, pp. 234–241. Springer (2015)

2015

Later among the works it cites.

Zettinig, O., Shah, A., Hennersperger, C., Eiber, M., Kroll, C., Kübler, H., Maurer, T., Milletari, F., Rackerseder, J., zu Berge, C.S., et al.: Multimodal image-guided prostate fusion biopsy based on automatic deformable registration. International journal of computer assisted radiology and surgery 10(12), 1997–2007 (2015)

2015

Later among the works it cites.

Cha, K.H., Hadjiiski, L., Samala, R.K., Chan, H.P., Caoili, E.M., Cohan, R.H.: Urinary bladder segmentation in ct urography using deep-learning convolutional neural network and level sets. Medical Physics 43(4), 1882–1896 (2016)