1 |
Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA Cancer J Clin 2017;67:7-30
DOI
|
2 |
Rosenkrantz AB, Oto A, Turkbey B, Westphalen AC. Prostate Imaging Reporting and Data System (PI-RADS), version 2: a critical look. AJR Am J Roentgenol 2016;206:1179-1183
DOI
|
3 |
Weinreb JC, Barentsz JO, Choyke PL, et al. PI-RADS Prostate Imaging - Reporting and Data System: 2015, version 2. Eur Urol 2016;69:16-40
DOI
|
4 |
Yoon JM, Choi MH, Lee YJ, Jung SE. Dynamic contrastenhanced MRI of the prostate: can auto-generated wash-in color map be useful in detecting focal lesion enhancement? Investig Magn Reson Imaging 2019;23:220-227
DOI
|
5 |
Choi MH, Jung SE, Park YH, Lee JY, Choi YJ. Multiparametric MRI of prostate cancer after biopsy: little impact of hemorrhage on tumor staging. Investig Magn Reson Imaging 2017;21:139-147
DOI
|
6 |
Giannini V, Mazzetti S, Vignati A, et al. A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging. Comput Med Imaging Graph 2015;46 Pt 2:219-226
DOI
|
7 |
Klein S, van der Heide UA, Lips IM, van Vulpen M, Staring M, Pluim JP. Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med Phys 2008;35:1407-1417
DOI
|
8 |
Gao Y, Sandhu R, Fichtinger G, Tannenbaum AR. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans Med Imaging 2010;29:1781-1794
DOI
|
9 |
Ghose S, Oliver A, Marti R, et al. A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Comput Methods Programs Biomed 2012;108:262-287
DOI
|
10 |
Tian Z, Liu L, Zhang Z, Fei B. Superpixel-based segmentation for 3D prostate MR images. IEEE Trans Med Imaging 2016;35:791-801
DOI
|
11 |
Wang H, Suh JW, Das SR, Pluta JB, Craige C, Yushkevich PA. Multi-atlas segmentation with joint label fusion. IEEE Trans Pattern Anal Mach Intell 2013;35:611-623
DOI
|
12 |
Chandra SS, Dowling JA, Greer PB, et al. Fast automated segmentation of multiple objects via spatially weighted shape learning. Phys Med Biol 2016;61:8070-8084
DOI
|
13 |
Greenham S, Dean J, Fu CK, et al. Evaluation of atlas-based auto-segmentation software in prostate cancer patients. J Med Radiat Sci 2014;61:151-158
DOI
|
14 |
Litjens G, Toth R, van de Ven W, et al. Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med Image Anal 2014;18:359-373
DOI
|
15 |
Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23 Suppl 1:S208-219
DOI
|