scholarly article | Q13442814 |
P50 | author | Tone F. Bathen | Q61104171 |
Mohammed R S Sunoqrot | Q89534811 | ||
P2093 | author name string | Radka Stoyanova | |
Mattijs Elschot | |||
Elise Sandsmark | |||
Kirsten M Selnæs | |||
Olmo Zavala-Romero | |||
Gabriel A Nketiah | |||
P2860 | cites work | Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy | Q91599257 |
MRI-Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer | Q91813783 | ||
PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images | Q92167793 | ||
Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control | Q92706494 | ||
Radiomics Based on Multiparametric Magnetic Resonance Imaging to Predict Extraprostatic Extension of Prostate Cancer | Q96954581 | ||
Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition | Q98184641 | ||
Computer-aided detection of prostate cancer in MRI. | Q51091670 | ||
Patient specific prostate segmentation in 3-d magnetic resonance images. | Q51335247 | ||
Comparison and evaluation of methods for liver segmentation from CT datasets. | Q51852049 | ||
Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. | Q51878500 | ||
ESTRO ACROP consensus guideline on CT- and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer. | Q52803212 | ||
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries | Q60142201 | ||
Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study | Q64093768 | ||
Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method | Q64109110 | ||
Segmentation of prostate and prostate zones using deep learning : A multi-MRI vendor analysis | Q90715494 | ||
Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation | Q91289458 | ||
A propagation-DNN: Deep combination learning of multi-level features for MR prostate segmentation | Q91320794 | ||
ESUR prostate MR guidelines 2012 | Q24620926 | ||
Statistical methods for assessing agreement between two methods of clinical measurement | Q26778461 | ||
Computer aided-diagnosis of prostate cancer on multiparametric MRI: a technical review of current research | Q27004458 | ||
Cancer statistics, 2014 | Q27861018 | ||
PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2 | Q29037908 | ||
Regularization Paths for Generalized Linear Models via Coordinate Descent | Q29614491 | ||
N4ITK: improved N3 bias correction | Q30432754 | ||
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge | Q34065836 | ||
Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance. | Q38064415 | ||
Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review | Q38369377 | ||
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth. | Q38823361 | ||
MRI of the prostate: interobserver agreement compared with histopathologic outcome after radical prostatectomy | Q46903275 | ||
Computational Radiomics System to Decode the Radiographic Phenotype | Q47623355 | ||
P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
P6216 | copyright status | copyrighted | Q50423863 |
P433 | issue | 9 | |
P577 | publication date | 2020-09-18 | |
P1433 | published in | Diagnostics | Q27726498 |
P1476 | title | A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI | |
P478 | volume | 10 |