Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation

Pagniez MA(2020)Predictive factors of missed clinically significant prostate cancers in men with negative magnetic resonance imaging: a systematic review and meta-analysis J Urol 204 24-32

Kasivisvanathan V(2020)Three-dimensional convolutional neural network for prostate MRI segmentation and comparison of prostate volume measurements by use of artificial neural network and ellipsoid formula AJR Am J Roentgenol 214 1229-1238

Puech P(2021)ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging Eur Radiol 31 302-313

Drumez E(2021)Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment Eur Radiol 76 340-351

Villers A(2019)Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2 Eur Urol 121 108716-1046

Olivier J(2019)Variability of manual segmentation of the prostate in axial T2-weighted MRI: a multi-reader study Eur J Radiol 116 102073-6105

Lee DK(2021)Uncertainty-aware temporal self-learning (UATS): semi-supervised learning for segmentation of prostate zones and beyond Artif Intell Med 12 71-1323

Sung DJ(2021)Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology Insights Imaging 23 1024-795

Kim CS(2016)Computer-aided detection of prostate cancer with MRI: technology and applications Acad Radiol 38 6093-736

Penzkofer T(2011)Zonal segmentation of prostate using multispectral magnetic resonance images Med Phys 44 1312-1089

Pagniez MA(2020)Predictive factors of missed clinically significant prostate cancers in men with negative magnetic resonance imaging: a systematic review and meta-analysis J Urol 204 24-32

Kasivisvanathan V(2020)Three-dimensional convolutional neural network for prostate MRI segmentation and comparison of prostate volume measurements by use of artificial neural network and ellipsoid formula AJR Am J Roentgenol 214 1229-1238

Puech P(2021)ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging Eur Radiol 31 302-313

Drumez E(2021)Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment Eur Radiol 76 340-351

Villers A(2019)Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2 Eur Urol 121 108716-1046

Olivier J(2019)Variability of manual segmentation of the prostate in axial T2-weighted MRI: a multi-reader study Eur J Radiol 116 102073-6105

Lee DK(2021)Uncertainty-aware temporal self-learning (UATS): semi-supervised learning for segmentation of prostate zones and beyond Artif Intell Med 12 71-1323

Sung DJ(2021)Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology Insights Imaging 23 1024-795

Kim CS(2016)Computer-aided detection of prostate cancer with MRI: technology and applications Acad Radiol 38 6093-736

Penzkofer T(2011)Zonal segmentation of prostate using multispectral magnetic resonance images Med Phys 44 1312-1089