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Prostate cancer is the most common cancer among US men.
Magnetic resonance imaging (MRI) anatomy of the prostate and application of MRI in radiotherapy planning
Villeirs, G.M., De Meerleer, G.O.: · 2007
Earlier work this paper cites.
Functional MR imaging of prostate cancer
Choi, Y.J., Kim, J.K., Kim, N., Kim, K.W., Choi, E.K., Cho, K.S.: · 2007
Earlier work this paper cites.
Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information
Klein, S., Van Der Heide, U.A., Lips, I.M., Van Vulpen, M., Staring, M., Pluim, J.P.: · 2008
Earlier work this paper cites.
Peripheral zone prostate cancers: location and intraprostatic patterns of spread at histopathology
Haffner, J., Potiron, E., Bouyé, S., Puech, P., Leroy, X., Lemaitre, L., Villers, A.: · 2009
Earlier work this paper cites.
Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model
Martin, S., Troccaz, J., Daanen, V.: · 2010
Earlier work this paper cites.
Large-scale machine learning with stochastic gradient descent
Bottou, L.: · 2010
Earlier work this paper cites.
The McNeal prostate: A review
Selman, S.H.: · 2011
Earlier work this paper cites.
Prostate cancer: multiparametric MR imaging for detection, localization, and staging
Hoeks, C.M., Barentsz, J.O., Hambrock, T., Yakar, D., Somford, D.M., Heijmink, S.W., et al.: · 2011
Earlier work this paper cites.
Fast Facts: Benign Prostatic Hyperplasia. 7 edn
Kirby, R., Gilling, R.: · 2011
Earlier work this paper cites.
Zonal segmentation of prostate using multispectral magnetic resonance images
Makni, N., Iancu, A., Colot, O., Puech, P., Mordon, S., Betrouni, N.: · 2011
Earlier work this paper cites.
Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI
Niaf, E., Rouvière, O., Mège-Lechevallier, F., Bratan, F., Lartizien, C.: · 2012
Earlier work this paper cites.
A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images
Ghose, S., Oliver, A., Martí, R., Lladó, X., Vilanova, J.C., Freixenet, J., et al.: · 2012
Earlier work this paper cites.
Simultaneous segmentation of prostatic zones using active appearance models with multiple coupled levelsets
Toth, R., Ribault, J., Gentile, J., Sperling, D., Madabhushi, A.: · 2013
Cited alongside, same era.
Dual optimization based prostate zonal segmentation in 3D MR images
Qiu, W., Yuan, J., Ukwatta, E., Sun, Y., Rajchl, M., Fenster, A.: · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
Cited alongside, same era.
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge
Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., et al.: · 2014
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al.: · 2014
Cited alongside, same era.
Automated prostate gland segmentation based on an unsupervised fuzzy c-means clustering technique using multispectral T1w and T2w MR imaging
Rundo, L., Militello, C., Russo, G., Garufi, A., Vitabile, S., Gilardi, M.C., Mauri, G.: · 2017
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Peripheral zone volume ratio (PZ-ratio) is relevant with biopsy results and can increase the accuracy of current diagnostic modality
Chang, Y., Chen, R., Yang, Q., Gao, X., Xu, C., Lu, J., Sun, Y.: · 2017
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SegNet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., Cipolla, R.: · 2017
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Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks
Clark, T., Zhang, J., Baig, S., Wong, A., Haider, M.A., Khalvati, F.: · 2017
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Cancer statistics, 2018
Siegel, R.L., Miller, K.D., Jemal, A.: · 2018
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Kingma, D., Welling, M.: · 2014
Cited alongside, same era.
Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review
Lemaître, G., Martí, R., Freixenet, J., Vilanova, J.C., Walker, P.M., Meriaudeau, F.: · 2015
Cited alongside, same era.
U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: · 2016
Cited alongside, same era.
Convolutional neural networks for medical image analysis: full training or fine tuning?
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: · 2016
Cited alongside, same era.
Deformable MR prostate segmentation via deep feature learning and sparse patch matching
Guo, Y., Gao, Y., Shen, D.: · 2016
Cited alongside, same era.
V-Net: fully convolutional neural networks for volumetric medical image segmentation
Milletari, F., Navab, N., Ahmadi, S.A.: · 2016
Cited alongside, same era.
Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy c-means clustering algorithm
Rundo, L., Militello, C., Russo, G., D’Urso, D., Valastro, L.M., Garufi, A., et al.: · 2018
Later among the works it cites.
Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing
AlBadawy, E.A., Saha, A., Mazurowski, M.A.: · 2018
Later among the works it cites.
GAN-based synthetic brain MR image generation
Han, C., Hayashi, H., Rundo, L., Araki, R., Shimoda, W., Muramatsu, S., et al.: · 2018
Later among the works it cites.
MedGA: a novel evolutionary method for image enhancement in medical imaging systems
Rundo, L., Tangherloni, A., Nobile, M.S., Militello, C., Besozzi, D., Mauri, G., Cazzaniga, P.: · 2019
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A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images
Bevilacqua, V., Brunetti, A., Guerriero, A., Trotta, G.F., Telegrafo, M., Moschetta, M.: · 2019
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U-Net: deep learning for cell counting, detection, and morphometry
Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., Böhm, A., Deubner, J., Jäckel, Z., Seiwald, K., et al.: · 2019
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