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Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging.
1903
Earlier work this paper cites.
doi:10.1145/1041613.1041614
K. W. Kolence, P. J. Kiviat, Software unit profiles & Kiviat figures, ACM SIGMETRICS Perform. Eval. Rev. 2 (3) (1973) 2–12 · 1973
Earlier work this paper cites.
R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, in: Proc. 14th International Joint Conference on Artificial Intelligence (IJCAI), Vol. 2, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1995, pp. 1137–1143
1995
Earlier work this paper cites.
doi:10.1109/ISSPA.2001.949797
Y. Zhang, A review of recent evaluation methods for image segmentation, in: Proc. IEEE International Symposium on Signal Processing and its Applications (ISSPA), Vol. 1, IEEE, 2001, pp. 148–151 · 2001
Earlier work this paper cites.
doi:10.1016/S1076-6332(03)00671-8
K. Zou, S. Warfield, A. Bharatha, C. Tempany, M. Kaus, S. Haker, et al., Statistical validation of image segmentation quality based on a spatial overlap index, Acad. Radiol. 11 (2) (2004) 178–189 · 2004
Earlier work this paper cites.
doi:10.1109/CVPR.2005.191
D. Freedman, T. Zhang, Interactive graph cut based segmentation with shape priors, in: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2005, pp. 755–762 · 2005
Earlier work this paper cites.
doi:10.1109/IEMBS.2005.1616166
A. Fenster, B. Chiu, Evaluation of segmentation algorithms for medical imaging, in: Proc. Annual International Conference of the Engineering in Medicine and Biology Society, IEEE, 2005, pp. 7186–7189 · 2005
Earlier work this paper cites.
doi:10.1007/s00330-005-2893-8
O. Rouvière, R. P. Hartman, D. Lyonnet, Prostate MR imaging at high-field strength: evolution or revolution?, Eur. Radiol. 16 (2) (2006) 276–284 · 2006
Earlier work this paper cites.
doi:10.1109/ISBI.2006.1624940
P. D. Allen, J. Graham, D. C. Williamson, C. E. Hutchinson, Differential segmentation of the prostate in MR images using combined 3D shape modelling and voxel classification, in: Proc. International Symposium on Biomedical Imaging (ISBI): Nano to Macro, IEEE, 2006, pp. 410–413 · 2006
Earlier work this paper cites.
doi:10.1016/j.eswa.2006.10.016
B. Diri, S. Albayrak, Visualization and analysis of classifiers performance in multi-class medical data, Expert Syst. Appl. 34 (1) (2008) 628–634 · 2006
Earlier work this paper cites.
doi:10.1109/TMI.2006.871549
K. Suzuki, H. Abe, H. MacMahon, K. Doi, Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN), IEEE Trans. Med. Imaging 25 (4) (2006) 406–416 · 2006
Earlier work this paper cites.
J. Demšar, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res. 7 (Jan) (2006) 1–30
2006
Earlier work this paper cites.
doi:10.1148/radiol.2441060425
S. W. Heijmink, J. J. Futterer, T. Hambrock, S. Takahashi, T. W. Scheenen, H. J. Huisman, et al., Prostate cancer: body-array versus endorectal coil MR imaging at 3 T—Comparison of image quality, localization, and staging performance, Radiology 244 (1) (2007) 184–195 · 2007
Earlier work this paper cites.
doi:10.1016/j.ejrad.2007.06.030
G. M. Villeirs, G. O. De Meerleer, Magnetic resonance imaging (MRI) anatomy of the prostate and application of MRI in radiotherapy planning, Eur. J. Radiol. 63 (3) (2007) 361–368 · 2007
Earlier work this paper cites.
doi:10.1148/rg.271065078
Y. J. Choi, J. K. Kim, N. Kim, K. W. Kim, E. K. Choi, K.-S. Cho, Functional MR imaging of prostate cancer, Radiographics 27 (1) (2007) 63–75 · 2007
Earlier work this paper cites.
doi:10.1016/j.patcog.2007.08.014
M. H. Masson, T. Denoeux, ECM: An evidential version of the fuzzy c-means algorithm, Pattern Recognit. 41 (4) (2008) 1384–1397 · 2007
Earlier work this paper cites.
doi:10.1097/RCT.0b013e3180683b99
C. K. Kim, B. K. Park, Update of prostate magnetic resonance imaging at 3 T, J. Comput. Assist. Tomogr. 32 (2) (2008) 163–172 · 2008
Earlier work this paper cites.
doi:10.1118/1.2842076
S. Klein, U. A. Van Der Heide, I. M. Lips, M. Van Vulpen, M. Staring, J. P. Pluim, Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information, Med. Phys. 35 (4) (2008) 1407–1417 · 2008
Earlier work this paper cites.
doi:10.1007/s11548-008-0247-0
S. Martin, V. Daanen, J. Troccaz, Atlas-based prostate segmentation using an hybrid registration, Int. J. Comput. Assist. Radiol. Surg. 3 (6) (2008) 485–492 · 2008
Earlier work this paper cites.
doi:10.1002/pros.20881
J. Haffner, E. Potiron, S. Bouyé, P. Puech, X. Leroy, L. Lemaitre, A. Villers, Peripheral zone prostate cancers: location and intraprostatic patterns of spread at histopathology, Prostate 69 (3) (2009) 276–282 · 2009
Earlier work this paper cites.
doi:10.1016/j.eswa.2009.10.036
T. Gandhi, B. K. Panigrahi, M. Bhatia, S. Anand, Expert model for detection of epileptic activity in EEG signature, Expert Syst. Appl. 37 (4) (2010) 3513–3520 · 2009
Earlier work this paper cites.
doi:10.1118/1.3315367
S. Martin, J. Troccaz, V. Daanen, Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model, Med. Phys. 37 (4) (2010) 1579–1590 · 2010
Earlier work this paper cites.
doi:10.1109/CVPR.2010.5539903
J. Yuan, E. Bae, X. C. Tai, A study on continuous max-flow and min-cut approaches, in: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2010, pp. 2217–2224 · 2010
Earlier work this paper cites.
V. Nair, G. E. Hinton, Rectified linear units improve restricted Boltzmann machines, in: Proc. 27th International Conference on Machine Learning (ICML), 2010, pp. 807–814
2010
Earlier work this paper cites.
doi:10.1007/978-3-7908-2604-3_16
L. Bottou, Large-scale machine learning with stochastic gradient descent, in: Proc. COMPSTAT’2010, Springer, 2010, pp. 177–186 · 2010
Earlier work this paper cites.
doi:10.1016/j.urology.2011.07.1395
S. H. Selman, The McNeal prostate: A review, Urology 78 (6) (2011) 1224–1228 · 2011
Cited alongside, same era.
doi:10.1148/radiol.11091822
C. M. Hoeks, J. O. Barentsz, T. Hambrock, D. Yakar, D. M. Somford, S. W. Heijmink, et al., Prostate cancer: multiparametric MR imaging for detection, localization, and staging, Radiology 261 (1) (2011) 46–66 · 2011
Cited alongside, same era.
R. Kirby, R. Gilling, Fast Facts: Benign Prostatic Hyperplasia, 7th Edition, Health Press Limited, Abingdon, UK, 2011
2011
Cited alongside, same era.
doi:10.1118/1.3651610
N. Makni, A. Iancu, O. Colot, P. Puech, S. Mordon, N. Betrouni, Zonal segmentation of prostate using multispectral magnetic resonance images, Med. Phys. 38 (11) (2011) 6093–6105 · 2011
Cited alongside, same era.
doi:10.1088/0031-9155/57/12/3833
E. Niaf, O. Rouvière, F. Mège-Lechevallier, F. Bratan, C. Lartizien, Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI, Phys. Med. Biol. 57 (12) (2012) 3833 · 2012
Cited alongside, same era.
doi:10.1016/j.cviu.2012.11.013
doi:10.1109/TMI.2016.2621185
M. Rajchl, M. C. Lee, O. Oktay, K. Kamnitsas, J. Passerat-Palmbach, W. Bai, et al., DeepCut: Object segmentation from bounding box annotations using convolutional neural networks, IEEE Trans. Med. Imaging 36 (2) (2017) 674–683 · 2016
Later among the works it cites.
doi:10.1016/j.media.2016.05.004
M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, et al., Brain tumor segmentation with deep neural networks, Med. Image Anal. 35 (2017) 18–31 · 2016
Later among the works it cites.
doi:10.1016/j.media.2016.10.004
K. Kamnitsas, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, et al., Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Med. Image Anal. 36 (2017) 61–78 · 2016
Later among the works it cites.
doi:10.1109/TPAMI.2016.2644615
V. Badrinarayanan, A. Kendall, R. Cipolla, SegNet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 39 (12) (2017) 2481–2495 · 2016
Later among the works it cites.
doi:10.18632/oncotarget.16753
Y. Chang, R. Chen, Q. Yang, X. Gao, C. Xu, J. Lu, Y. Sun, Peripheral zone volume ratio (PZ-ratio) is relevant with biopsy results and can increase the accuracy of current diagnostic modality, Oncotarget 8 (21) (2017) 34836 · 2017
Later among the works it cites.
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alphaXiv is searching for related work…
R. Toth, J. Ribault, J. Gentile, D. Sperling, A. Madabhushi, Simultaneous segmentation of prostatic zones using active appearance models with multiple coupled levelsets, Comput. Vis. Image Underst. 117 (9) (2013) 1051–1060 · 2012
Cited alongside, same era.
doi:10.1117/12.911778
Y. Yin, S. V. Fotin, S. Periaswamy, J. Kunz, H. Haldankar, N. Muradyan, et al., Fully automated 3D prostate central gland segmentation in MR images: a LOGISMOS based approach, in: Medical Imaging: Image Processing, Vol. 8314 of Proc. SPIE, International Society for Optics and Photonics, 2012, p. 83143B · 2012
Cited alongside, same era.
doi:10.1016/j.media.2014.02.009
W. Qiu, J. Yuan, E. Ukwatta, Y. Sun, M. Rajchl, A. Fenster, Dual optimization based prostate zonal segmentation in 3D MR images, Med. Image Anal. 18 (4) (2014) 660–673 · 2014
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., Generative adversarial nets, in: Proc. Advances in Neural Information Processing Systems (NIPS), 2014, pp. 2672–2680, http://papers.nips.cc/paper/5423-generative-adversarial-nets
2014
Cited alongside, same era.
2014
Cited alongside, same era.
doi:10.1016/j.compbiomed.2015.02.009
G. Lemaître, R. Martí, J. Freixenet, J. C. Vilanova, P. M. Walker, F. Meriaudeau, Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review, Comput. Biol. Med. 60 (2015) 8–31 · 2015
Cited alongside, same era.
doi:10.1097/RLI.0000000000000163
T. W. Scheenen, A. B. Rosenkrantz, M. A. Haider, J. J. Fütterer, Multiparametric magnetic resonance imaging in prostate cancer management: current status and future perspectives, Invest. Radiol. 50 (9) (2015) 594–600 · 2015
Cited alongside, same era.
doi:10.3390/info8020049
L. Rundo, C. Militello, G. Russo, A. Garufi, S. Vitabile, M. C. Gilardi, G. Mauri, Automated prostate gland segmentation based on an unsupervised fuzzy c-means clustering technique using multispectral T1w and T2w MR imaging, Information 8 (2) (2017) 49 · 2017
Later among the works it cites.
doi:10.1016/j.media.2017.07.005
G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, et al., A survey on deep learning in medical image analysis, Med. Image Anal. 42 (2017) 60–88 · 2017
Later among the works it cites.
doi:10.1007/978-3-319-67389-9_26
J. Sun, Y. Shi, Y. Gao, D. Shen, A point says a lot: An interactive segmentation method for MR prostate via one-point labeling, in: Proc. International Workshop on Machine Learning in Medical Imaging, Springer, 2017, pp. 220–228 · 2017
Later among the works it cites.
doi:10.1016/j.neucom.2017.09.084
H. Jia, Y. Xia, Y. Song, W. Cai, M. Fulham, D. D. Feng, Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging, Neurocomputing 275 (2018) 1358–1369 · 2017
Later among the works it cites.
doi:10.1016/j.media.2017.08.006
X. Yang, C. Liu, Z. Wang, J. Yang, H. Le Min, L. Wang, K.-T. T. Cheng, Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI, Med. Image Anal. 42 (2017) 212–227 · 2017
Later among the works it cites.
doi:10.1109/TMI.2017.2789181
Z. Wang, C. Liu, D. Cheng, L. Wang, X. Yang, K.-T. Cheng, Automated detection of clinically significant prostate cancer in mp-MRI images based on an end-to-end deep neural network, IEEE Trans. Med. Imaging 37 (5) (2018) 1127–1139 · 2017
Later among the works it cites.
doi:10.1117/1.JMI.4.4.041307
T. Clark, J. Zhang, S. Baig, A. Wong, M. A. Haider, F. Khalvati, Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks, J. Med. Imaging 4 (4) (2017) 041307 · 2017
Later among the works it cites.
doi:10.1007/s11047-017-9636-z
L. Rundo, C. Militello, G. Russo, S. Vitabile, M. C. Gilardi, G. Mauri, GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model, Nat. Comput. (2017) 1–16 (In press) · 2017
Later among the works it cites.
doi:10.1038/sdata.2017.124
F. Prior, K. Smith, A. Sharma, J. Kirby, L. Tarbox, K. Clark, et al., The public cancer radiology imaging collections of The Cancer Imaging Archive, Sci. Data 4 (2017) 170124 · 2017
Later among the works it cites.
doi:10.1073/pnas.1715832114
D. M. Pelt, J. A. Sethian, A mixed-scale dense convolutional neural network for image analysis, Proc. Natl. Acad. Sci. 115 (2) (2018) 254–259 · 2018
Later among the works it cites.
doi:10.1007/978-3-319-56904-8_3
L. Rundo, C. Militello, G. Russo, D. D’Urso, L. M. Valastro, A. Garufi, et al., Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy c-means clustering algorithm, in: Multidisciplinary Approaches to Neural Computing, Vol. 69 of Smart Innovation, Systems and Technologies, Springer, 2018, pp. 23–37 · 2018
Later among the works it cites.
doi:10.1002/ima.22253
L. Rundo, C. Militello, A. Tangherloni, G. Russo, S. Vitabile, M. C. Gilardi, G. Mauri, NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor mri using an unsupervised machine learning technique, Int. J. Imaging Syst. Technol. 28 (1) (2018) 21–37 · 2018
Later among the works it cites.
doi:10.1002/mp.12752
E. A. AlBadawy, A. Saha, M. A. Mazurowski, Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing, Med. Phys. 45 (3) (2018) 1150–1158 · 2018
Later among the works it cites.
doi:10.1109/TBME.2018.2828137
Z. Yan, X. Yang, K.-T. Cheng, Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation, IEEE Trans. Biomed. Eng. 65 (9) (2018) 1912–1923 · 2018
Later among the works it cites.
doi:10.1007/978-3-030-00937-3_57
X. Yang, H. Dou, R. Li, X. Wang, C. Bian, S. Li, D. Ni, P.-A. Heng, Generalizing deep models for ultrasound image segmentation, in: Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, 2018, pp. 497–505 · 2018
Later among the works it cites.
doi:10.1016/j.neucom.2018.05.103
W. Yao, Z. Zeng, C. Lian, H. Tang, Pixel-wise regression using U-Net and its application on pansharpening, Neurocomputing 312 (2018) 364–371 · 2018
Later among the works it cites.
doi:10.1109/ISBI.2018.8363678
C. Han, H. Hayashi, L. Rundo, R. Araki, W. Shimoda, S. Muramatsu, et al., GAN-based synthetic brain MR image generation, in: Proc. IEEE International Symposium on Biomedical Imaging (ISBI), 2018, pp. 734–738 · 2018
Later among the works it cites.
doi:10.3322/caac.21551
R. L. Siegel, K. D. Miller, A. Jemal, Cancer statistics, 2019, CA Cancer J. Clin. 69 (1) (2019) 7–34 · 2019
Closest in time.
doi:10.1016/j.media.2019.01.012
J. Schlemper, O. Oktay, M. Schaap, M. Heinrich, B. Kainz, B. Glocker, D. Rueckert, Attention gated networks: learning to leverage salient regions in medical images, Med. Image Anal. 53 (2019) 197–207 · 2019
Closest in time.