Computer-Aided Detection of Prostate Cancer in MRI
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Nico Karssemeijer | Geert J. S. Litjens | Jelle O. Barentsz | Henkjan J. Huisman | Oscar Debats | H. Huisman | N. Karssemeijer | G. Litjens | J. Barentsz | O. Debats
[1] Andrew Hayen,et al. 2220 MAGNETIC RESONANCE IMAGING DETECTS SIGNIFICANT PROSTATE CANCER AND COULD BE USED TO REDUCE UNNECESSARY BIOPSIES: INITIAL RESULTS FROM A PROSPECTIVE TRIAL , 2013 .
[2] P Tiwari,et al. Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection , 2012, NMR in biomedicine.
[3] Theodorus H van der Kwast,et al. A critical analysis of the tumor volume threshold for clinically insignificant prostate cancer using a data set of a randomized screening trial. , 2011, The Journal of urology.
[4] R. Lenkinski,et al. Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2‐weighted MR imagery , 2012, Journal of magnetic resonance imaging : JMRI.
[5] Stefan Klein,et al. Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. , 2008, Medical physics.
[6] H. Huisman,et al. Interpatient variation in normal peripheral zone apparent diffusion coefficient: effect on the prediction of prostate cancer aggressiveness. , 2012, Radiology.
[7] Thomas Hambrock,et al. Simulated required accuracy of image registration tools for targeting high-grade cancer components with prostate biopsies , 2013, European Radiology.
[8] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[9] William Wells,et al. Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier. , 2003, Medical physics.
[10] Thomas Hambrock,et al. Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI , 2010, Physics in medicine and biology.
[11] M. Knopp,et al. Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.
[12] Nico Karssemeijer,et al. Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach , 2012, Medical Imaging.
[13] Qiang Li,et al. Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. , 2003, Medical physics.
[14] N Karssemeijer,et al. Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis , 2012, Physics in medicine and biology.
[15] Kazuro Sugimura,et al. Prostate cancer detection with 3 T MRI: Comparison of diffusion‐weighted imaging and dynamic contrast‐enhanced MRI in combination with T2‐weighted imaging , 2010, Journal of magnetic resonance imaging : JMRI.
[16] Nico Karssemeijer,et al. A Pattern Recognition Approach to Zonal Segmentation of the Prostate on MRI , 2012, MICCAI.
[17] J. Fütterer,et al. ESUR prostate MR guidelines 2012 , 2012, European Radiology.
[18] Anant Madabhushi,et al. Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS , 2013, Medical Image Anal..
[19] Heinz-Otto Peitgen,et al. Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans , 2006, IEEE Transactions on Medical Imaging.
[20] Nicholas Petrick,et al. CT colonography computer-aided polyp detection: Effect on radiologist observers of polyp identification by CAD on both the supine and prone scans. , 2010, Academic radiology.
[21] Emilie Niaf,et al. Influence of imaging and histological factors on prostate cancer detection and localisation on multiparametric MRI: a prospective study , 2013, European Radiology.
[22] A. Evans,et al. Prostate cancer detection with multi‐parametric MRI: Logistic regression analysis of quantitative T2, diffusion‐weighted imaging, and dynamic contrast‐enhanced MRI , 2009, Journal of magnetic resonance imaging : JMRI.
[23] Thomas Hambrock,et al. Prospective assessment of prostate cancer aggressiveness using 3-T diffusion-weighted magnetic resonance imaging-guided biopsies versus a systematic 10-core transrectal ultrasound prostate biopsy cohort. , 2012, European urology.
[24] N Karssemeijer,et al. Computer aided detection of masses in mammograms as decision support. , 2006, The British journal of radiology.
[25] A. Jemal,et al. Cancer statistics, 2013 , 2013, CA: a cancer journal for clinicians.
[26] Max A. Viergever,et al. Label Fusion in Atlas-Based Segmentation Using a Selective and Iterative Method for Performance Level Estimation (SIMPLE) , 2010, IEEE Transactions on Medical Imaging.
[27] H. Huisman,et al. Accurate estimation of pharmacokinetic contrast‐enhanced dynamic MRI parameters of the prostate , 2001, Journal of magnetic resonance imaging : JMRI.
[28] Carole Lartizien,et al. Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI , 2012, Physics in medicine and biology.
[29] Ayman El-Baz,et al. A diffusion-weighted imaging based diagnostic system for early detection of prostate cancer , 2013 .
[30] Colin Studholme,et al. Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change , 2006, IEEE Transactions on Medical Imaging.
[31] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[32] Adam S. Kibel,et al. Screening and Prostate-Cancer Mortality in a Randomized European Study , 2009 .