M. Suga, T. Aga, K. Minato (2004)
Development of a magnetic resonance elastic microscope systemThe 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1
Wei Liu, B. Turkbey, J. Sénégas, Prof. Remmele, Sheng Xu, J. Kruecker, M. Bernardo, B. Wood, P. Pinto, P. Choyke (2011)
Accelerated T2 mapping for characterization of prostate cancerMagnetic Resonance in Medicine, 65
T. Graham, G. Box, N. Tunariu, M. Crespo, T. Spinks, S. Miranda, G. Attard, J. Bono, S. Eccles, F. Davies, S. Robinson (2014)
Preclinical evaluation of imaging biomarkers for prostate cancer bone metastasis and response to cabozantinib.Journal of the National Cancer Institute, 106 4
A. Shukla-Dave, H. Hricak, N. Ishill, C. Moskowitz, M. Drobnjak, V. Reuter, K. Zakian, P. Scardino, C. Cordon-Cardo (2009)
Correlation of MR imaging and MR spectroscopic imaging findings with Ki-67, phospho-Akt, and androgen receptor expression in prostate cancer.Radiology, 250 3
Suga (2004)
Development of a magnetic resonance elastic microscope systemConf Proc IEEE Eng Med Biol Soc, 2
J. Egger (2013)
PCG-Cut: Graph Driven Segmentation of the Prostate Central GlandPLoS ONE, 8
P. Gibbs, G. Liney, M. Pickles, B. Zelhof, G. Rodrigues, L. Turnbull (2009)
Correlation of ADC and T2 Measurements With Cell Density in Prostate Cancer at 3.0 TeslaInvestigative Radiology, 44
S. Viswanath, N. Bloch, J. Chappelow, R. Toth, N. Rofsky, E. Genega, R. Lenkinski, A. Madabhushi (2012)
Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2‐weighted MR imageryJournal of Magnetic Resonance Imaging, 36
(1999)
Modern Information Retrieval
E. Gibson, M. Gaed, J. Gómez, M. Moussa, S. Pautler, J. Chin, C. Crukley, G. Bauman, A. Fenster, A. Ward (2013)
3D prostate histology image reconstruction: Quantifying the impact of tissue deformation and histology section locationJournal of Pathology Informatics, 4
Xin Liu, I. Yetik (2011)
Automated prostate cancer localization without the need for peripheral zone extraction using multiparametric MRI.Medical physics, 38 6
Ian Chan, W. Wells, R. Mulkern, S. Haker, Jianqin Zhang, K. Zou, S. Maier, C. Tempany (2003)
Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier.Medical physics, 30 9
F. Cornud, M. Rouanne, F. Beuvon, D. Eiss, T. Flam, M. Liberatore, M. Zerbib, N. Delongchamps (2012)
Endorectal 3D T2-weighted 1mm-slice thickness MRI for prostate cancer staging at 1.5Tesla: should we reconsider the indirects signs of extracapsular extension according to the D'Amico tumor risk criteria?European journal of radiology, 81 4
B. Fei, W. Ng, S. Chauhan, C. Kwoh (2001)
The safety issues of medical roboticsReliab. Eng. Syst. Saf., 73
(2015)
PNAS Plus Significance StatementsProceedings of the National Academy of Sciences, 112
C. Kalavagunta, Xiangming Zhou, S. Schmechel, G. Metzger (2015)
Registration of in vivo prostate MRI and pseudo‐whole mount histology using Local Affine Transformations guided by Internal Structures (LATIS)Journal of Magnetic Resonance Imaging, 41
S. Mazzetti, Antonio Gliozzi, C. Bracco, F. Russo, D. Regge, Michele Stasi (2012)
Comparison between PUN and Tofts models in the quantification of dynamic contrast-enhanced MR imagingPhysics in Medicine and Biology, 57
S. Viswanath, B. Bloch, M. Rosen, J. Chappelow, R. Toth, N. Rofsky, R. Lenkinski, E. Genega, A. Kalyanpur, A. Madabhushi (2009)
Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI, 7260
J. Epstein, M. Amin, V. Reuter, P. Humphrey (2017)
Contemporary Gleason Grading of Prostatic Carcinoma: An Update With Discussion on Practical Issues to Implement the 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic CarcinomaThe American Journal of Surgical Pathology, 41
P. Quann, D. Jarrard, Wei Huang (2010)
Current prostate biopsy protocols cannot reliably identify patients for focal therapy: correlation of low-risk prostate cancer on biopsy with radical prostatectomy findings.International journal of clinical and experimental pathology, 3 4
I. Song, C. Kim, B. Park, W. Park (2010)
Assessment of response to radiotherapy for prostate cancer: value of diffusion-weighted MRI at 3 T.AJR. American journal of roentgenology, 194 6
B. Fei, Hesheng Wang, Chunying Wu, S. Chiu (2010)
Choline PET for Monitoring Early Tumor Response to Photodynamic TherapyJournal of Nuclear Medicine, 51
Tim Cootes, A. Hill, C. Taylor, J. Haslam (1993)
The Use of Active Shape Models for Locating Structures in Medical Images
G. Fiard, N. Hohn, J. Descotes, J. Rambeaud, J. Troccaz, J. Long (2013)
Targeted MRI-guided prostate biopsies for the detection of prostate cancer: initial clinical experience with real-time 3-dimensional transrectal ultrasound guidance and magnetic resonance/transrectal ultrasound image fusion.Urology, 81 6
L. Dickinson, H. Ahmed, C. Allen, J. Barentsz, B. Carey, J. Futterer, S. Heijmink, P. Hoskin, A. Kirkham, A. Padhani, R. Persad, P. Puech, S. Punwani, A. Sohaib, B. Tombal, A. Villers, J. Meulen, M. Emberton (2011)
Magnetic resonance imaging for the detection, localisation, and characterisation of prostate cancer: recommendations from a European consensus meeting.European urology, 59 4
Adam Anderson, J. Xie, J. Pizzonia, R. Bronen, D. Spencer, J. Gore (2000)
Effects of cell volume fraction changes on apparent diffusion in human cells.Magnetic resonance imaging, 18 6
Helen Xu, A. Lasso, P. Guion, A. Krieger, A. Kaushal, Anurag Singh, P. Pinto, J. Coleman, R. Grubb, J. Lattouf, C. Ménard, L. Whitcomb, G. Fichtinger (2013)
Accuracy analysis in MRI-guided robotic prostate biopsyInternational Journal of Computer Assisted Radiology and Surgery, 8
J. Fütterer, J. Barentsz (2012)
MRI-guided and robotic-assisted prostate biopsyCurrent Opinion in Urology, 22
C. Moore, N. Robertson, N. Arsanious, Thomas Middleton, A. Villers, L. Klotz, S. Taneja, M. Emberton (2013)
Image-guided prostate biopsy using magnetic resonance imaging-derived targets: a systematic review.European urology, 63 1
B. Fei, Z. Lee, D. Boll, Jeffery Duerk, D. Sodee, J. Lewin, D. Wilson (2004)
Registration and fusion of SPECT, high-resolution MRI, and interventional MRI for thermal ablation of prostate cancerIEEE Transactions on Nuclear Science, 51
G. Sonn, D. Margolis, L. Marks (2014)
Target detection: magnetic resonance imaging-ultrasound fusion-guided prostate biopsy.Urologic oncology, 32 6
Meijuan Yang, Xuelong Li, B. Turkbey, P. Choyke, Pingkun Yan (2013)
Prostate Segmentation in MR Images Using Discriminant Boundary FeaturesIEEE Transactions on Biomedical Engineering, 60
A. Wibmer, H. Hricak, Tatsuo Gondo, K. Matsumoto, H. Veeraraghavan, D. Fehr, Junting Zheng, D. Goldman, C. Moskowitz, S. Fine, V. Reuter, J. Eastham, E. Sala, H. Vargas (2015)
Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scoresEuropean Radiology, 25
J. Epstein, W. Allsbrook, M. Amin, L. Egevad (2005)
The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic CarcinomaThe American Journal of Surgical Pathology, 29
Hong Li, K. Sugimura, Y. Kaji, Y. Kitamura, M. Fujii, I. Hara, Mayumi Tachibana (2006)
Conventional MRI capabilities in the diagnosis of prostate cancer in the transition zone.AJR. American journal of roentgenology, 186 3
I. Schoots, M. Roobol, D. Nieboer, C. Bangma, E. Steyerberg, M. Hunink (2015)
Magnetic resonance imaging-targeted biopsy may enhance the diagnostic accuracy of significant prostate cancer detection compared to standard transrectal ultrasound-guided biopsy: a systematic review and meta-analysis.European urology, 68 3
J. Nowak, U. Malzahn, A. Baur, U. Reichelt, T. Franiel, B. Hamm, T. Durmuş (2016)
The value of ADC, T2 signal intensity, and a combination of both parameters to assess Gleason score and primary Gleason grades in patients with known prostate cancerActa Radiologica, 57
J. Stember, F. Deng, S. Taneja, A. Rosenkrantz (2014)
Pilot study of a novel tool for input‐free automated identification of transition zone prostate tumors using T2‐ and diffusion‐weighted signal and textural featuresJournal of Magnetic Resonance Imaging, 40
L. Arrivé, S. Derhy, S. mouhadi, L. Monnier-Cholley, Y. Menu, C. Becker (2015)
Noncontrast Magnetic Resonance LymphographyJournal of Reconstructive Microsurgery, 32
G. Litjens, R. Toth, W. Ven, C. Hoeks, Sjoerd Kerkstra, B. Ginneken, G. Vincent, G. Guillard, N. Birbeck, Jindang Zhang, R. Strand, F. Malmberg, Yangming Ou, C. Davatzikos, M. Kirschner, F. Jung, Jing Yuan, W. Qiu, Qinquan Gao, P. Edwards, B. Maan, F. Heijden, S. Ghose, J. Mitra, J. Dowling, D. Barratt, H. Huisman, A. Madabhushi (2014)
Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challengeMedical image analysis, 18 2
G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer, H. Huisman (2014)
Computer-Aided Detection of Prostate Cancer in MRIIEEE Transactions on Medical Imaging, 33
J. Lee, I. Thomas, R. Nolley, M. Ferrari, J. Brooks, J. Leppert (2015)
Biologic differences between peripheral and transition zone prostate cancerThe Prostate, 75
M. Kass, A. Witkin, Demetri Terzopoulos (2004)
Snakes: Active contour modelsInternational Journal of Computer Vision, 1
P. Vos, J. Barentsz, N. Karssemeijer, H. Huisman (2012)
Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysisPhysics in Medicine and Biology, 57
S. Klein, U. Heide, I. Lips, M. Vulpen, M. Staring, J. Pluim (2008)
Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information.Medical physics, 35 4
E. Gibson, M. Gaed, J. Gomez, M. Moussa, C. Romagnoli, S. Pautler, J. Chin, C. Crukley, G. Bauman, A. Fenster, A. Ward (2014)
3 D prostate histology reconstruction : An evaluation of image-based and fiducial-based algorithms
V. Shah, T. Pohida, B. Turkbey, H. Mani, M. Merino, P. Pinto, P. Choyke, M. Bernardo (2009)
A method for correlating in vivo prostate magnetic resonance imaging and histopathology using individualized magnetic resonance-based molds.The Review of scientific instruments, 80 10
L. Breiman (2001)
Random ForestsMachine Learning, 45
F. Khalvati, A. Wong, M. Haider (2015)
Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature modelsBMC Medical Imaging, 15
L. Lemaitre (2006)
[Imaging of the prostate].Journal de radiologie, 87 2 Pt 2
Author manuscript; available in PMC
Sa-ying Li, Min Chen, Wen-chao Wang, Wei-feng Zhao, Jianye Wang, Xuna Zhao, Cheng Zhou (2011)
A feasibility study of MR elastography in the diagnosis of prostate cancer at 3.0TActa Radiologica, 52
T. Kobus, T. Hambrock, C. Kaa, A. Wright, J. Barentsz, A. Heerschap, T. Scheenen (2011)
In vivo assessment of prostate cancer aggressiveness using magnetic resonance spectroscopic imaging at 3 T with an endorectal coil.European urology, 60 5
D. Langer, T. Kwast, A. Evans, J. Trachtenberg, B. Wilson, M. Haider (2009)
Prostate cancer detection with multi‐parametric MRI: Logistic regression analysis of quantitative T2, diffusion‐weighted imaging, and dynamic contrast‐enhanced MRIJournal of Magnetic Resonance Imaging, 30
J. Friedman (2000)
Special Invited Paper-Additive logistic regression: A statistical view of boostingAnnals of Statistics, 28
P. Iu (2013)
ESUR prostate MR guidelines.European radiology, 23 8
S. Sinha, U. Sinha (2004)
In vivo diffusion tensor imaging of the human prostateMagnetic Resonance in Medicine, 52
Emilie Niaf, C. Lartizien, F. Bratan, L. Roche, M. Rabilloud, F. Mege-Lechevallier, O. Rouvière (2014)
Prostate focal peripheral zone lesions: characterization at multiparametric MR imaging--influence of a computer-aided diagnosis system.Radiology, 271 3
F. Fennessy, Andrey Fedorov, Sandeep Gupta, E. Schmidt, C. Tempany, R. Mulkern (2012)
Practical considerations in T1 mapping of prostate for dynamic contrast enhancement pharmacokinetic analyses.Magnetic resonance imaging, 30 9
Image features for prostate cancer detection. (a) With prostate cancer superposed in green
(2014)
B) images show a well-defined T2 hypointense lesion in the peripheral zone (arrow) with corresponding high signal on DWI (C) and low signal on the ADC map (D)
S. Viswanath, B. Bloch, J. Chappelow, Pratik Patel, N. Rofsky, R. Lenkinski, E. Genega, A. Madabhushi (2011)
Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI, 7963
C. Meyer, B. Moffat, Kyle Kuszpit, P. Bland, P. Mckeever, T. Johnson, T. Chenevert, A. Rehemtulla, B. Ross (2006)
A Methodology for Registration of a Histological Slide and In Vivo MRI Volume Based on Optimizing Mutual InformationMolecular Imaging, 5
Virendra Kumar, Yuhua Gu, Satrajit Basu, A. Berglund, S. Eschrich, M. Schabath, K. Forster, H. Aerts, A. Dekker, D. Fenstermacher, Dmitry Goldgof, L. Hall, P. Lambin, Y. Balagurunathan, R. Gatenby, R. Gillies (2012)
Radiomics: the process and the challenges.Magnetic resonance imaging, 30 9
Y. Mazaheri, L. Bokacheva, D. Kroon, O. Akin, H. Hricak, D. Chamudot, S. Fine, J. Koutcher (2010)
Semi‐automatic deformable registration of prostate MR images to pathological slicesJournal of Magnetic Resonance Imaging, 32
R. Siegel, K. Miller, A. Jemal (2015)
Cancer statistics, 2015CA: A Cancer Journal for Clinicians, 65
R. Zwiggelaar, Yanong Zhu, Stuart Williams (2003)
Semi-automatic Segmentation of the Prostate
R. Mohan, P. Schellhammer (2011)
Treatment options for localized prostate cancer.American family physician, 84 4
Shijun Wang, Karen Burtt, B. Turkbey, P. Choyke, R. Summers (2014)
Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current ResearchBioMed Research International, 2014
Zhiqiang Tian, Lizhi Liu, Zhenfeng Zhang, Jianru Xue, B. Fei (2015)
A supervoxel-based segmentation method for prostate MR images, 9413
B. Fei, Z. Lee, J. Duerk, D. Wilson (2003)
Image Registration for Interventional MRI Guided Procedures: Interpolation Methods, Similarity Measurements, and Applications to the Prostate
Jin Yamamura, G. Salomon, Ralph Buchert, A. Hohenstein, J. Graessner, Hartwig Huland, M. Graefen, Gerhard Adam, U. Wedegaetner (2010)
MR Imaging of Prostate Cancer: Diffusion Weighted Imaging and (3D) Hydrogen 1 (1H) MR Spectroscopy in Comparison with HistologyRadiology Research and Practice, 2011
J. Asten, V. Cuijpers, C. Kaa, C. Soede-Huijbregts, J. Witjes, A. Verhofstad, A. Heerschap (2008)
High resolution magic angle spinning NMR spectroscopy for metabolic assessment of cancer presence and Gleason score in human prostate needle biopsiesMagnetic Resonance Materials in Physics, Biology and Medicine, 21
Ansje Fortuin, R. Smeenk, H. Meijer, A. Witjes, J. Barentsz (2014)
Lymphotropic Nanoparticle-enhanced MRI in Prostate Cancer: Value and Therapeutic PotentialCurrent Urology Reports, 15
D. Flores-Tapia, G. Thomas, N. Venugopal, B. McCurdy, S. Pistorius (2008)
Semi automatic MRI prostate segmentation based on wavelet multiscale products2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Yahui Peng, Yulei Jiang, Cheng Yang, J. Brown, T. Antic, I. Sethi, C. Schmid-Tannwald, M. Giger, S. Eggener, A. Oto (2013)
Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study.Radiology, 267 3
K. Çam, S. Yucel, L. Turkeri, A. Akdaş (2002)
Accuracy of transrectal ultrasound guided prostate biopsy: Histopathological correlation to matched prostatectomy specimensInternational Journal of Urology, 9
Jussi Toivonen, H. Merisaari, Marko Pesola, P. Taimen, P. Boström, T. Pahikkala, H. Aronen, I. Jambor (2015)
Mathematical models for diffusion‐weighted imaging of prostate cancer using b values up to 2000 s/mm2: Correlation with Gleason score and repeatability of region of interest analysisMagnetic Resonance in Medicine, 74
R. Sinkus, J. Lorenzen, D. Schrader, M. Lorenzen, Michael Dargatz, D. Holz (2000)
High-resolution tensor MR elastography for breast tumour detection.Physics in medicine and biology, 45 6
B. Fei, Hesheng Wang, Joseph Meyers, D. Feyes, N. Oleinick, J. Duerk (2007)
High‐field magnetic resonance imaging of the response of human prostate cancer to Pc 4‐based photodynamic therapy in an animal modelLasers in Surgery and Medicine, 39
J. Barentsz, J. Richenberg, R. Clements, P. Choyke, S. Verma, G. Villeirs, O. Rouvière, V. Løgager, J. Fütterer (2012)
ESUR prostate MR guidelines 2012European Radiology, 22
H. Akbari, B. Fei (2012)
3D ultrasound image segmentation using wavelet support vector machines.Medical physics, 39 6
U. Vovk, F. Pernus, B. Likar (2004)
MRI intensity inhomogeneity correction by combining intensity and spatial informationPhysics in Medicine & Biology, 49
S. Venkatesh, M. Yin, J. Glockner, N. Takahashi, P. Araoz, J. Talwalkar, R. Ehman (2008)
MR elastography of liver tumors: preliminary results.AJR. American journal of roentgenology, 190 6
А. Коробкин, М. Шария, А. Чабан, Г. Восканян, А. Винаров (2015)
Информативность мультипараметрического МР-исследования в выявлении рака предстательной железы. Классификация pi-rads (prostate imaging-reporting and data system)Research'n Practical Medicine Journal
G. Litjens, J. Barentsz, N. Karssemeijer, H. Huisman (2015)
Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRIEuropean Radiology, 25
B. Fei, Xiaofeng Yang, J. Nye, J. Aarsvold, N. Raghunath, Morgan Cervo, Rebecca Stark, C. Meltzer, J. Votaw (2012)
MR∕PET quantification tools: registration, segmentation, classification, and MR-based attenuation correction.Medical physics, 39 10
P. Puech, O. Rouvière, R. Renard-Penna, A. Villers, P. Devos, M. Colombel, M. Bitker, X. Leroy, F. Mege-Lechevallier, E. Compérat, A. Ouzzane, L. Lemaitre (2013)
Prostate cancer diagnosis: multiparametric MR-targeted biopsy with cognitive and transrectal US-MR fusion guidance versus systematic biopsy--prospective multicenter study.Radiology, 268 2
Shiteng Suo, Xiao-xi Chen, Lianming Wu, Xiaofei Zhang, Qiuying Yao, Yu Fan, He Wang, Jian-rong Xu (2014)
Non-Gaussian water diffusion kurtosis imaging of prostate cancer.Magnetic resonance imaging, 32 5
J. Chappelow, A. Madabhushi, M. Rosen, J. Tomaszeweski, M. Feldman (2007)
Multimodal image registration of ex vivo 4 Tesla MRI with whole mount histology for prostate cancer detection, 6512
A. Dinh, R. Souchon, C. Melodelima, F. Bratan, F. Mege-Lechevallier, M. Colombel, O. Rouvière (2015)
Characterization of prostate cancer using T2 mapping at 3T: a multi-scanner study.Diagnostic and interventional imaging, 96 4
J. Kwak, Sheng Xu, B. Wood, B. Turkbey, P. Choyke, P. Pinto, Shijun Wang, R. Summers (2015)
Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging.Medical physics, 42 5
C. Orczyk, H. Rusinek, A. Rosenkrantz, A. Mikheev, F. Deng, J. Melamed, S. Taneja (2013)
Preliminary experience with a novel method of three-dimensional co-registration of prostate cancer digital histology and in vivo multiparametric MRI.Clinical radiology, 68 12
J. Sled, A. Zijdenbos, Alan Evans (1998)
A nonparametric method for automatic correction of intensity nonuniformity in MRI dataIEEE Transactions on Medical Imaging, 17
C. Meyer, B. Ma, L. Kunju, M. Davenport, M. Piert (2013)
Challenges in accurate registration of 3-D medical imaging and histopathology in primary prostate cancerEuropean Journal of Nuclear Medicine and Molecular Imaging, 40
Renaud Lopes, Antoine Ayache, N. Makni, Philippe Puech, A. Villers, Serge Mordon, N. Betrouni (2010)
Prostate cancer characterization on MR images using fractal features.Medical physics, 38 1
L. Boesen, E. Chabanova, V. Løgager, I. Balslev, H. Thomsen (2015)
Apparent diffusion coefficient ratio correlates significantly with prostate cancer gleason score at final pathologyJournal of Magnetic Resonance Imaging, 42
E. Halpern, D. Cochlin, B. Goldberg (2002)
Imaging of the prostate
P. Tiwari, M. Rosen, A. Madabhushi (2009)
A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS).Medical physics, 36 9
T. Chan, L. Vese (2001)
Active contours without edgesIEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 10 2
Y. Mazaheri, A. Shukla-Dave, H. Hricak, S. Fine, Jingbo Zhang, G. Inurrigarro, C. Moskowitz, N. Ishill, V. Reuter, K. Touijer, K. Zakian, J. Koutcher (2008)
Prostate cancer: identification with combined diffusion-weighted MR imaging and 3D 1H MR spectroscopic imaging--correlation with pathologic findings.Radiology, 246 2
Y. Artan, I. Yetik (2012)
Prostate Cancer Localization Using Multiparametric MRI based on Semisupervised Techniques With Automated Seed InitializationIEEE Transactions on Information Technology in Biomedicine, 16
Y. Takayama, R. Kishimoto, S. Hanaoka, Hiroi Nonaka, S. Kandatsu, H. Tsuji, H. Tsujii, H. Ikehira, T. Obata (2008)
ADC value and diffusion tensor imaging of prostate cancer: Changes in carbon‐ion radiotherapyJournal of Magnetic Resonance Imaging, 27
B. Fei, Z. Lee, D. Boll, J. Duerk, J. Lewin, D. Wilson (2003)
Image Registration and Fusion for Interventional MRI Guided Thermal Ablation of the Prostate Cancer
Andrey Fedorov, J. Fluckiger, G. Ayers, Xia Li, Sandeep Gupta, C. Tempany, R. Mulkern, T. Yankeelov, F. Fennessy (2014)
A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: a step towards practical implementation.Magnetic resonance imaging, 32 4
K. Kitajima, Satoru Takahashi, Yoshiko Ueno, T. Yoshikawa, Y. Ohno, M. Obara, H. Miyake, M. Fujisawa, K. Sugimura (2012)
Clinical utility of apparent diffusion coefficient values obtained using high b‐value when diagnosing prostate cancer using 3 tesla MRI: Comparison between ultra‐high b‐value (2000 s/mm2) and standard high b‐value (1000 s/mm2)Journal of Magnetic Resonance Imaging, 36
Dong Lee, K. Koo, Seung Lee, K. Rha, Y. Choi, S. Hong, B. Chung (2013)
Tumor lesion diameter on diffusion weighted magnetic resonance imaging could help predict insignificant prostate cancer in patients eligible for active surveillance: preliminary analysis.The Journal of urology, 190 4
Hesheng Wang, B. Fei (2011)
An MR image-guided, voxel-based partial volume correction method for PET images.Medical physics, 39 1
G. Collewet, M. Strzelecki, F. Mariette (2004)
Influence of MRI acquisition protocols and image intensity normalization methods on texture classification.Magnetic resonance imaging, 22 1
M. Roethke, T. Kuder, T. Kuru, M. Fenchel, B. Hadaschik, F. Laun, H. Schlemmer, B. Stieltjes (2015)
Evaluation of Diffusion Kurtosis Imaging Versus Standard Diffusion Imaging for Detection and Grading of Peripheral Zone Prostate CancerInvestigative Radiology, 50
T. Scheenen, S. Heijmink, S. Roell, C. Kaa, B. Knipscheer, J. Witjes, J. Barentsz, A. Heerschap (2007)
Three-dimensional proton MR spectroscopy of human prostate at 3 T without endorectal coil: feasibility.Radiology, 245 2
R. Toth, N. Shih, J. Tomaszewski, M. Feldman, Oliver Kutter, Daphne Yu, John Paulus, Ginaluca Paladini, A. Madabhushi (2014)
Histostitcher™: An informatics software platform for reconstructing whole-mount prostate histology using the extensible imaging platform frameworkJournal of Pathology Informatics, 5
U. Hamhaber, D. Klatt, S. Papazoglou, M. Hollmann, J. Stadler, I. Sack, J. Bernarding, J. Braun (2010)
In vivo magnetic resonance elastography of human brain at 7 T and 1.5 TJournal of Magnetic Resonance Imaging, 32
N. Dubrawsky (1989)
Cancer statisticsCA: A Cancer Journal for Clinicians, 39
V. Shah, B. Turkbey, H. Mani, Y. Pang, T. Pohida, M. Merino, P. Pinto, P. Choyke, M. Bernardo (2012)
Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging.Medical physics, 39 7Part1
R. Toth, A. Madabhushi (2012)
Multifeature Landmark-Free Active Appearance Models: Application to Prostate MRI SegmentationIEEE Transactions on Medical Imaging, 31
A. Oto, Cheng Yang, A. Kayhan, M. Tretiakova, T. Antic, C. Schmid-Tannwald, S. Eggener, G. Karczmar, W. Stadler (2011)
Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis.AJR. American journal of roentgenology, 197 6
P. Vos, T. Hambrock, J. Barentsz, H. Huisman (2009)
Automated Calibration for Computerized Analysis of Prostate Lesions Using Pharmacokinetic Magnetic Resonance ImagesMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 12 Pt 2
O. Donati, Y. Mazaheri, A. Afaq, H. Vargas, Junting Zheng, C. Moskowitz, H. Hricak, O. Akin (2014)
Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient.Radiology, 271 1
P. Tiwari, A. Madabhushi, M. Rosen (2007)
A Hierarchical Unsupervised Spectral Clustering Scheme for Detection of Prostate Cancer from Magnetic Resonance Spectroscopy (MRS)Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 10 Pt 2
K. Murase (2004)
Efficient method for calculating kinetic parameters using T1‐weighted dynamic contrast‐enhanced magnetic resonance imagingMagnetic Resonance in Medicine, 51
M. Siddiqui, S. Rais-Bahrami, B. Turkbey, A. George, J. Rothwax, Nabeel Shakir, Chinonyerem Okoro, Dima Raskolnikov, H. Parnes, W. Linehan, M. Merino, R. Simon, P. Choyke, B. Wood, P. Pinto (2015)
Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer.JAMA, 313 4
P. Puech, N. Betrouni, R. Viard, A. Villers, X. Leroy, L. Lemaitre (2007)
Prostate cancer computer-assisted diagnosis software using dynamic contrast-enhanced MRI2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
K. Zakian, K. Sircar, H. Hricak, Hui-Ni Chen, A. Shukla-Dave, S. Eberhardt, M. Muruganandham, Lanie Ebora, M. Kattan, V. Reuter, P. Scardino, J. Koutcher (2005)
Correlation of proton MR spectroscopic imaging with gleason score based on step-section pathologic analysis after radical prostatectomy.Radiology, 234 3
P. Lambin, Emmanuel Rios-Velazquez, R. Leijenaar, S. Carvalho, R. Stiphout, P. Granton, C. Zegers, R. Gillies, R. Boellard, A. Dekker, H. Aerts (2012)
Radiomics: extracting more information from medical images using advanced feature analysis.European journal of cancer, 48 4
Boykov (2001)
Fast approximate energy minimization via graph cutsIEEE Trans Pattern Anal Mach Intell, 23
J. Ewing, H. Bagher-Ebadian (2013)
Model selection in measures of vascular parameters using dynamic contrast‐enhanced MRI: experimental and clinical applicationsNMR in Biomedicine, 26
Jaime Neto, D. Parente (2013)
Multiparametric magnetic resonance imaging of the prostate.Magnetic resonance imaging clinics of North America, 21 2
P. Vos, T. Hambrock, C. Kaa, J. Fütterer, J. Barentsz, H. Huisman (2008)
Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI.Medical physics, 35 3
Puech (2007)
Prostate cancer computer-assisted diagnosis software using dynamic contrast-enhanced MRIConf Proc IEEE Eng Med Biol Soc, 2007
María García-Martín, M. Adrados, M. Ortega, I. González, P. López-Larrubia, J. Viano, J. García-Segura (2011)
Quantitative 1H MR spectroscopic imaging of the prostate gland using LCModel and a dedicated basis‐set: Correlation with histologic findingsMagnetic Resonance in Medicine, 65
C. Hoeks, T. Hambrock, Derya Yakar, C. Kaa, T. Feuth, J. Witjes, J. Fütterer, J. Barentsz (2013)
Transition zone prostate cancer: detection and localization with 3-T multiparametric MR imaging.Radiology, 266 1
O. Akin, E. Sala, C. Moskowitz, K. Kuroiwa, N. Ishill, D. Pucar, P. Scardino, H. Hricak (2006)
Transition zone prostate cancers: features, detection, localization, and staging at endorectal MR imaging.Radiology, 239 3
Y. Sung, H. Kwon, Bum‐Woo Park, G. Cho, C. Lee, K. Cho, J. Kim (2011)
Prostate cancer detection on dynamic contrast-enhanced MRI: computer-aided diagnosis versus single perfusion parameter maps.AJR. American journal of roentgenology, 197 5
Hesheng Wang, B. Fei (2010)
Diffusion‐weighted MRI for monitoring tumor response to photodynamic therapyJournal of Magnetic Resonance Imaging, 32
Hyunjin Park, M. Piert, Asra Khan, R. Shah, H. Hussain, J. Siddiqui, T. Chenevert, C. Meyer (2008)
Registration methodology for histological sections and in vivo imaging of human prostate.Academic radiology, 15 8
Junqian Xu, P. Humphrey, A. Kibel, A. Snyder, V. Narra, J. Ackerman, Sheng-Kwei Song (2009)
Magnetic resonance diffusion characteristics of histologically defined prostate cancer in humansMagnetic Resonance in Medicine, 61
A. Padhani, A. Makris, Peter Gall, D. Collins, N. Tunariu, J. Bono (2014)
Therapy monitoring of skeletal metastases with whole‐body diffusion MRIJournal of Magnetic Resonance Imaging, 39
Kuei Lee, D. Bradley, M. Hussain, C. Meyer, T. Chenevert, J. Jacobson, T. Johnson, C. Galbán, A. Rehemtulla, K. Pienta, B. Ross (2007)
A feasibility study evaluating the functional diffusion map as a predictive imaging biomarker for detection of treatment response in a patient with metastatic prostate cancer to the bone.Neoplasia, 9 12
B. Djavan, V. Ravery, A. Zlotta, P. Dobronski, Michael Dobrovits, M. Fakhari, C. Seitz, M. Susani, A. Borkowski, L. Boccon‐Gibod, C. Schulman, M. Marberger (2001)
Prospective evaluation of prostate cancer detected on biopsies 1, 2, 3 and 4: when should we stop?The Journal of urology, 166 5
A. Cameron, F. Khalvati, M. Haider, A. Wong (2016)
MAPS: A Quantitative Radiomics Approach for Prostate Cancer DetectionIEEE Transactions on Biomedical Engineering, 63
Pratik Patel, J. Chappelow, J. Tomaszeweski, M. Feldman, M. Rosen, N. Shih, A. Madabhushi (2011)
Spatially weighted mutual information (SWMI) for registration of digitally reconstructed ex vivo whole mount histology and in vivo prostate MRI2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Sheng Xu, J. Kruecker, B. Turkbey, N. Glossop, Anurag Singh, P. Choyke, P. Pinto, B. Wood (2008)
Real-time MRI-TRUS fusion for guidance of targeted prostate biopsiesComputer Aided Surgery, 13
D. Mumford, J. Shah (1989)
Optimal approximations by piecewise smooth functions and associated variational problemsCommunications on Pure and Applied Mathematics, 42
S. Ginsburg, S. Viswanath, B. Bloch, N. Rofsky, E. Genega, R. Lenkinski, A. Madabhushi (2015)
Novel PCA‐VIP scheme for ranking MRI protocols and identifying computer‐extracted MRI measurements associated with central gland and peripheral zone prostate tumorsJournal of Magnetic Resonance Imaging, 41
Nikolaos Dikaios, Jokha Alkalbani, M. Abd-Alazeez, H. Sidhu, A. Kirkham, H. Ahmed, M. Emberton, A. Freeman, S. Halligan, S. Taylor, D. Atkinson, S. Punwani (2015)
Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRIEuropean Radiology, 25
P. Vos, T. Hambrock, Jelle Barenstz, H. Huisman (2010)
Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRIPhysics in Medicine & Biology, 55
Yanong Zhu, Stuart Williams, R. Zwiggelaar (2007)
A hybrid ASM approach for sparse volumetric data segmentationPattern Recognition and Image Analysis, 17
J. Mitra, S. Ghose, D. Sidibé, R. Martí, A. Oliver, X. Lladó, J. Vilanova, J. Comet, F. Mériaudeau (2012)
Joint probability of shape and image similarities to retrieve 2D TRUS-MR slice correspondence for prostate biopsy2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
D. McGrath, R. Vlad, W. Foltz, K. Brock (2010)
Technical note: fiducial markers for correlation of whole-specimen histopathology with MR imaging at 7 tesla.Medical physics, 37 5
A. Arani, M. Rosa, E. Ramsay, D. Plewes, M. Haider, Rajiv Chopra (2013)
Incorporating endorectal MR elastography into multi‐parametric MRI for prostate cancer imaging: Initial feasibility in volunteersJournal of Magnetic Resonance Imaging, 38
(2009)
Sobel-Kirsch feature. (d) second order statistics (contrast inverse moment). (e) Corresponding time-intensity curves for CaP (red) and benign (blue) regions are shown based on DCE-MRI data
José Molina, Lei Zheng, M. Sertdemir, D. Dinter, S. Schönberg, M. Rädle (2014)
Incremental Learning with SVM for Multimodal Classification of Prostatic AdenocarcinomaPLoS ONE, 9
W. Qiu, Jing Yuan, E. Ukwatta, Yue Sun, Martin Rajchl, A. Fenster (2014)
Dual optimization based prostate zonal segmentation in 3D MR imagesMedical image analysis, 18 4
Qiang Li, S. Sone, K. Doi (2003)
Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans.Medical physics, 30 8
H. Huisman, M. Engelbrecht, J. Barentsz (2001)
Accurate estimation of pharmacokinetic contrast‐enhanced dynamic MRI parameters of the prostateJournal of Magnetic Resonance Imaging, 13
P. Zámecnik, M. Schouten, A. Krafft, F. Maier, H. Schlemmer, J. Barentsz, M. Bock, J. Fütterer (2014)
Automated real-time needle-guide tracking for fast 3-T MR-guided transrectal prostate biopsy: a feasibility study.Radiology, 273 3
Fehr (2015)
Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance imagesProc Natl Acad Sci USA, 112
Francisco Oliveira, J. Tavares (2014)
Medical image registration: a reviewComputer Methods in Biomechanics and Biomedical Engineering, 17
A. Gliozzi, S. Mazzetti, P. Delsanto, D. Regge, M. Stasi (2011)
Phenomenological universalities: a novel tool for the analysis of dynamic contrast enhancement in magnetic resonance imagingPhysics in Medicine & Biology, 56
Yahui Peng, Yulei Jiang, T. Antic, M. Giger, S. Eggener, A. Oto (2014)
Validation of quantitative analysis of multiparametric prostate MR images for prostate cancer detection and aggressiveness assessment: a cross-imager study.Radiology, 271 2
S. Anwar, Zahid Khan, Rana Hamid, Fahd Haroon, R. Sayani, M. Beg, Y. Khattak (2014)
Assessment of Apparent Diffusion Coefficient Values as Predictor of Aggressiveness in Peripheral Zone Prostate Cancer: Comparison with Gleason ScoreISRN Radiology, 2014
Yuri Boykov, O. Veksler, R. Zabih (2001)
Fast approximate energy minimization via graph cutsProceedings of the Seventh IEEE International Conference on Computer Vision, 1
B. Turkbey, Maria Merino, E. Gallardo, V. Shah, O. Aras, M. Bernardo, E. Mena, D. Daar, A. Rastinehad, W. Linehan, B. Wood, P. Pinto, P. Choyke (2014)
Comparison of endorectal coil and nonendorectal coil T2W and diffusion‐weighted MRI at 3 Tesla for localizing prostate cancer: Correlation with whole‐mount histopathologyJournal of Magnetic Resonance Imaging, 39
P. Tiwari, J. Kurhanewicz, A. Madabhushi (2013)
Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRSMedical image analysis, 17 2
S. Viswanath, Nicholas Bloch, N. Rofsky, R. Lenkinski, Elisabeth Genega, J. Chappelow, R. Toth, A. Madabhushi (2008)
A Comprehensive Segmentation, Registration, and Cancer Detection Scheme on 3 Tesla In VivoProstate DCE-MRIMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 11 Pt 1
J. Yacoub, A. Oto, F. Miller (2014)
MR imaging of the prostate.Radiologic clinics of North America, 52 4
O. Rouvière, M. Papillard, N. Girouin, R. Boutier, M. Rabilloud, B. Riche, F. Mege-Lechevallier, M. Colombel, A. Gelet (2012)
Is it possible to model the risk of malignancy of focal abnormalities found at prostate multiparametric MRI?European Radiology, 22
D. Mahapatra, J. Buhmann (2014)
Prostate MRI Segmentation Using Learned Semantic Knowledge and Graph CutsIEEE Transactions on Biomedical Engineering, 61
J. Chappelow, B. Bloch, N. Rofsky, E. Genega, R. Lenkinski, W. DeWolf, A. Madabhushi (2011)
Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information.Medical physics, 38 4
Y. Artan, M. Haider, D. Langer, T. Kwast, A. Evans, Yongyi Yang, M. Wernick, J. Trachtenberg, I. Yetik (2010)
Prostate Cancer Localization With Multispectral MRI Using Cost-Sensitive Support Vector Machines and Conditional Random FieldsIEEE Transactions on Image Processing, 19
X. Wang, Y. Qian, B. Liu, L. Cao, Y. Fan, J. Zhang, Y. Yu (2014)
High-b-value diffusion-weighted MRI for the detection of prostate cancer at 3 T.Clinical radiology, 69 11
Jie Zhang, Jianjun Xiu, Yin Dong, Muwen Wang, Xue Han, Ye-jun Qin, Zhaoqin Huang, S. Cai, Xianshun Yuan, Qingwei Liu (2014)
Magnetic resonance imaging‑directed biopsy improves the prediction of prostate cancer aggressiveness compared with a 12‑core transrectal ultrasound‑guided prostate biopsy.Molecular medicine reports, 9 5
P. Huang, Cheng-Hsiung Lee (2009)
Automatic Classification for Pathological Prostate Images Based on Fractal AnalysisIEEE Transactions on Medical Imaging, 28
Tahir Durmus, Alexander Baur, Bernd Hamm (2014)
Multiparametric Magnetic Resonance Imaging in the Detection of Prostate CancerAktuelle Urologie, 45
T. Hambrock, P. Vos, C. Kaa, J. Barentsz, H. Huisman (2013)
Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance.Radiology, 266 2
P. Tofts, G. Brix, D. Buckley, J. Evelhoch, E. Henderson, M. Knopp, H. Larsson, Ting-Yim Lee, N. Mayr, G. Parker, R. Port, June Taylor, R. Weisskoff (1999)
Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbolsJournal of Magnetic Resonance Imaging, 10
N. Tustison, B. Avants, P. Cook, Yuanjie Zheng, Alexander Egan, Paul Yushkevich, J. Gee (2010)
N4ITK: Improved N3 Bias CorrectionIEEE Transactions on Medical Imaging, 29
Dongjiao Lv, Xuemei Guo, Xiaoying Wang, Jue Zhang, Jing Fang (2009)
Computerized characterization of prostate cancer by fractal analysis in MR imagesJournal of Magnetic Resonance Imaging, 30
A. McKnight, J. Kugel, P. Rossman, A. Manduca, L. Hartmann, R. Ehman (2002)
MR elastography of breast cancer: preliminary results.AJR. American journal of roentgenology, 178 6
Anna Brown, Osama Elbuluk, Francesca Mertan, S. Sankineni, D. Margolis, B. Wood, P. Pinto, P. Choyke, B. Turkbey (2015)
Recent advances in image-guided targeted prostate biopsyAbdominal Imaging, 40
Emilie Niaf, O. Rouvière, F. Mege-Lechevallier, F. Bratan, C. Lartizien (2012)
Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRIPhysics in Medicine and Biology, 57
H. Thoeny, M. Triantafyllou, F. Birkhaeuser, J. Froehlich, D. Tshering, T. Binser, A. Fleischmann, P. Vermathen, U. Studer (2009)
Combined ultrasmall superparamagnetic particles of iron oxide-enhanced and diffusion-weighted magnetic resonance imaging reliably detect pelvic lymph node metastases in normal-sized nodes of bladder and prostate cancer patients.European urology, 55 4
F. Yamauchi, T. Penzkofer, Andrey Fedorov, F. Fennessy, R. Chu, S. Maier, C. Tempany, R. Mulkern, L. Panych (2015)
Prostate cancer discrimination in the peripheral zone with a reduced field-of-view T(2)-mapping MRI sequence.Magnetic resonance imaging, 33 5
Daniel Saman, A. Lemieux, M. Lutfiyya, M. Lipsky (2014)
A review of the current epidemiology and treatment options for prostate cancer.Disease-a-month : DM, 60 4
Maryam Samiee, Gabriel Thomas, Reza Fazel-Rezai (2006)
Semi-Automatic Prostate Segmentation of MR Images Based on Flow Orientation2006 IEEE International Symposium on Signal Processing and Information Technology
Kai Zhao, Chengyan Wang, Juan Hu, Xuedong Yang, He Wang, Fei-yu Li, Xiaodong Zhang, Jue Zhang, Xiaoying Wang (2015)
Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network modelScience China Life Sciences, 58
K. Tanimoto, K. Yoshikawa, T. Obata, H. Ikehira, T. Shiraishi, Kazuhiro Watanabe, T. Saga, J. Mizoe, T. Kamada, A. Kato, M. Miyazaki (2009)
Role of glucose metabolism and cellularity for tumor malignancy evaluation using FDG-PET/CT and MRINuclear Medicine Communications, 31
Xin Liu, D. Langer, M. Haider, Yongyi Yang, M. Wernick, I. Yetik (2009)
Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and ClassIEEE Transactions on Medical Imaging, 28
Emilie Niaf, Rémi Flamary, O. Rouvière, C. Lartizien, S. Canu (2014)
Kernel-Based Learning From Both Qualitative and Quantitative Labels: Application to Prostate Cancer Diagnosis Based on Multiparametric MR ImagingIEEE Transactions on Image Processing, 23
V. Panebianco, F. Barchetti, A. Sciarra, A. Marcantonio, C. Zini, S. Salciccia, F. Collettini, V. Gentile, B. Hamm, C. Catalano (2013)
In vivo 3D neuroanatomical evaluation of periprostatic nerve plexus with 3T-MR Diffusion Tensor Imaging.European journal of radiology, 82 10
M. Jacobs, M. Jacobs, J. Windham, J. Windham, H. Soltanian-Zadeh, H. Soltanian-Zadeh, H. Soltanian-Zadeh, D. Peck, R. Knight, R. Knight (1999)
Registration and warping of magnetic resonance images to histological sections.Medical physics, 26 8
P. Tiwari, S. Viswanath, J. Kurhanewicz, A. Sridhar, A. Madabhushi (2012)
Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detectionNMR in Biomedicine, 25
Hesheng Wang, B. Fei (2013)
Nonrigid point registration for 2D curves and 3D surfaces and its various applicationsPhysics in Medicine and Biology, 58
C. Kim, B. Park, Bohyun Kim (2010)
High-b-value diffusion-weighted imaging at 3 T to detect prostate cancer: comparisons between b values of 1,000 and 2,000 s/mm2.AJR. American journal of roentgenology, 194 1
J. Sosna, I. Pedrosa, W. DeWolf, H. Mahallati, R. Lenkinski, N. Rofsky (2004)
MR imaging of the prostate at 3 Tesla: comparison of an external phased-array coil to imaging with an endorectal coil at 1.5 Tesla.Academic radiology, 11 8
Yuri Boykov, M. Jolly (2001)
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D imagesProceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 1
P. Puech, N. Betrouni, N. Makni, A. Dewalle-Vignion, A. Villers, L. Lemaitre (2008)
Computer-assisted diagnosis of prostate cancer using DCE-MRI data: design, implementation and preliminary resultsInternational Journal of Computer Assisted Radiology and Surgery, 4
Edward Lawrence, S. Tang, T. Barrett, B. Koo, D. Goldman, A. Warren, R. Axell, A. Doble, F. Gallagher, V. Gnanapragasam, C. Kastner, E. Sala (2014)
Prostate cancer: performance characteristics of combined T2W and DW-MRI scoring in the setting of template transperineal re-biopsy using MR-TRUS fusionEuropean Radiology, 24
W. Qiu, Jing Yuan, E. Ukwatta, Yue Sun, Martin Rajchl, A. Fenster (2014)
Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR ImagesIEEE Transactions on Medical Imaging, 33
S. Derhy, S. mouhadi, A. Ruiz, L. Azizi, Y. Menu, L. Arrivé (2013)
Non-contrast 3D MR lymphography of retroperitoneal lymphatic aneurysmal dilatation: a continuous spectrum of change from normal variants to cystic lymphangiomaInsights into Imaging, 4
A. Arani, D. Plewes, A. Krieger, Rajiv Chopra (2011)
The feasibility of endorectal MR elastography for prostate cancer localizationMagnetic Resonance in Medicine, 66
A. Wetter, F. Nensa, C. Lipponer, N. Guberina, T. Olbricht, M. Schenck, T. Schlosser, M. Gratz, T. Lauenstein (2015)
High and ultra-high b-value diffusion-weighted imaging in prostate cancer: a quantitative analysisActa Radiologica, 56
U. Vovk, F. Pernus, B. Likar (2007)
A Review of Methods for Correction of Intensity Inhomogeneity in MRIIEEE Transactions on Medical Imaging, 26
Weinreb (2016)
PI-RADS Prostate Imaging-Reporting and Data System: 2015, version 2Eur Urol, 69
R. Sinkus, M. Tanter, T. Xydeas, S. Catheline, J. Bercoff, M. Fink (2005)
Viscoelastic shear properties of in vivo breast lesions measured by MR elastography.Magnetic resonance imaging, 23 2
G. Litjens, O. Debats, W. Ven, N. Karssemeijer, H. Huisman (2012)
A Pattern Recognition Approach to Zonal Segmentation of the Prostate on MRIMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 15 Pt 2
A. Padhani, C. Gapinski, D. Macvicar, GEOFFREY Parker, J. Suckling, P. Revell, M. Leach, D. Dearnaley, J. Husband (2000)
Dynamic contrast enhanced MRI of prostate cancer: correlation with morphology and tumour stage, histological grade and PSA.Clinical radiology, 55 2
S. Ghose, A. Oliver, R. Martí, X. Lladó, J. Vilanova, J. Freixenet, J. Mitra, D. Sidibé, F. Mériaudeau (2012)
A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography imagesComputer methods and programs in biomedicine, 108 1
Shu Liao, Yaozong Gao, A. Oto, D. Shen (2013)
Representation Learning: A Unified Deep Learning Framework for Automatic Prostate MR SegmentationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 16 Pt 2
L. Matulewicz, J. Jansen, L. Bokacheva, H. Vargas, O. Akin, S. Fine, A. Shukla-Dave, J. Eastham, H. Hricak, J. Koutcher, K. Zakian (2014)
Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imagingJournal of Magnetic Resonance Imaging, 40
M. Pokorny, M. Rooij, E. Duncan, F. Schröder, R. Parkinson, J. Barentsz, Les Thompson (2014)
Prospective study of diagnostic accuracy comparing prostate cancer detection by transrectal ultrasound-guided biopsy versus magnetic resonance (MR) imaging with subsequent MR-guided biopsy in men without previous prostate biopsies.European urology, 66 1
T. Hambrock, D. Somford, H. Huisman, I. Oort, J. Witjes, C. Kaa, T. Scheenen, J. Barentsz (2011)
Relationship between apparent diffusion coefficients at 3.0-T MR imaging and Gleason grade in peripheral zone prostate cancer.Radiology, 259 2
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