Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration
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Yipeng Hu | Nooshin Ghavami | Ester Bonmati | Eli Gibson | Dean C Barratt | Mark Emberton | Caroline M Moore | N. Ghavami | C. Moore | D. Barratt | Yipeng Hu | M. Emberton | E. Bonmati | E. Gibson
[1] Ian A. Donaldson,et al. The SmartTarget Biopsy Trial: A Prospective, Within-person Randomised, Blinded Trial Comparing the Accuracy of Visual-registration and Magnetic Resonance Imaging/Ultrasound Image-fusion Targeted Biopsies for Prostate Cancer Risk Stratification , 2019, European urology.
[2] Nooshin Ghavami,et al. Integration of spatial information in convolutional neural networks for automatic segmentation of intraoperative transrectal ultrasound images , 2018, Journal of medical imaging.
[3] Lena Maier-Hein,et al. How to Exploit Weaknesses in Biomedical Challenge Design and Organization , 2018, MICCAI.
[4] Dean C. Barratt,et al. Automatic slice segmentation of intraoperative transrectal ultrasound images using convolutional neural networks , 2018, Medical Imaging.
[5] Inderbir S. Gill,et al. Prostate evaluation for clinically important disease: Sampling using image-guidance or not? (The PRECISION study, NCT02380027) , 2018 .
[6] Dean C. Barratt,et al. Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks , 2018, IEEE Transactions on Medical Imaging.
[7] Tom Vercauteren,et al. Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network , 2017, Journal of medical imaging.
[8] Marc Modat,et al. Label-driven weakly-supervised learning for multimodal deformarle image registration , 2017, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[9] Quoc V. Le,et al. Don't Decay the Learning Rate, Increase the Batch Size , 2017, ICLR.
[10] Parashkev Nachev,et al. Computer Methods and Programs in Biomedicine NiftyNet: a deep-learning platform for medical imaging , 2022 .
[11] G. Valdes,et al. Comment on ‘Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study’ , 2018, Physics in medicine and biology.
[12] Dean C. Barratt,et al. Designing image segmentation studies: Statistical power, sample size and reference standard quality , 2017, Medical Image Anal..
[13] Masoom A. Haider,et al. Fully Deep Convolutional Neural Networks for Segmentation of the Prostate Gland in Diffusion-Weighted MR Images , 2017, ICIAR.
[14] Sébastien Ourselin,et al. Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks , 2017, BrainLes@MICCAI.
[15] Lawrence H. Staib,et al. Learning Non‐rigid Deformations for Robust, Constrained Point‐based Registration in Image‐Guided MR‐TRUS Prostate Intervention , 2017, Medical Image Anal..
[16] Sébastien Ourselin,et al. On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task , 2017, IPMI.
[17] S. Rais-Bahrami,et al. Factors predicting prostate cancer upgrading on magnetic resonance imaging–targeted biopsy in an active surveillance population , 2017, Cancer.
[18] Nassir Navab,et al. ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network , 2017, Biomedical optics express.
[19] Brandon Whitcher,et al. The Effect of Dutasteride on Magnetic Resonance Imaging Defined Prostate Cancer: MAPPED—A Randomized, Placebo Controlled, Double‐Blind Clinical Trial , 2017, The Journal of urology.
[20] Xiao Han,et al. MR‐based synthetic CT generation using a deep convolutional neural network method , 2017, Medical physics.
[21] Bo Du,et al. Deeply-supervised CNN for prostate segmentation , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[22] Zhiqiang Tian,et al. Deep convolutional neural network for prostate MR segmentation , 2017, Medical Imaging.
[23] M. Parmar,et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confi rmatory study , 2018 .
[24] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[25] Hao Chen,et al. Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images , 2017, AAAI.
[26] Pavlo M. Radiuk,et al. Impact of Training Set Batch Size on the Performance of Convolutional Neural Networks for Diverse Datasets , 2017, Information Technology and Management Science.
[27] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Seyed-Ahmad Ahmadi,et al. Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound , 2016, Comput. Vis. Image Underst..
[29] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[30] Seyed-Ahmad Ahmadi,et al. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).
[31] W. Catalona,et al. Diagnostic Value of Guided Biopsies: Fusion and Cognitive-registration Magnetic Resonance Imaging Versus Conventional Ultrasound Biopsy of the Prostate. , 2016, Urology.
[32] A Hayen,et al. The Diagnostic Performance of Multiparametric Magnetic Resonance Imaging to Detect Significant Prostate Cancer. , 2016, The Journal of urology.
[33] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[34] Fang-Ming Deng,et al. Relationship Between Prebiopsy Multiparametric Magnetic Resonance Imaging (MRI), Biopsy Indication, and MRI-ultrasound Fusion-targeted Prostate Biopsy Outcomes. , 2016, European urology.
[35] P. Pinto,et al. Magnetic Resonance Imaging-Ultrasound Fusion-Guided Prostate Biopsy: Review of Technology, Techniques, and Outcomes , 2016, Current Urology Reports.
[36] R. Saouaf,et al. 3D high‐resolution diffusion‐weighted MRI at 3T: Preliminary application in prostate cancer patients undergoing active surveillance protocol for low‐risk prostate cancer , 2016, Magnetic resonance in medicine.
[37] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Masoom A. Haider,et al. Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes , 2015, Journal of Digital Imaging.
[39] J. Bernhard,et al. Prostate Cancer Volume Estimation by Combining Magnetic Resonance Imaging and Targeted Biopsy Proven Cancer Core Length: Correlation with Cancer Volume. , 2015, The Journal of urology.
[40] A. Ouzzane,et al. Magnetic Resonance Imaging Targeted Biopsy Improves Selection of Patients Considered for Active Surveillance for Clinically Low Risk Prostate Cancer Based on Systematic Biopsies. , 2015, The Journal of urology.
[41] Nassir Navab,et al. Multimodal image-guided prostate fusion biopsy based on automatic deformable registration , 2015, International Journal of Computer Assisted Radiology and Surgery.
[42] P. Choyke,et al. Use of serial multiparametric magnetic resonance imaging in the management of patients with prostate cancer on active surveillance. , 2015, Urologic oncology.
[43] Saining Xie,et al. Holistically-Nested Edge Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[44] Florian Jung,et al. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge , 2014, Medical Image Anal..
[45] Yipeng Hu,et al. The PICTURE study -- prostate imaging (multi-parametric MRI and Prostate HistoScanning™) compared to transperineal ultrasound guided biopsy for significant prostate cancer risk evaluation. , 2014, Contemporary clinical trials.
[46] C. Moore,et al. MRI-targeted prostate biopsy: a review of technique and results , 2013, Nature Reviews Urology.
[47] P. Choyke,et al. Accuracy of multiparametric magnetic resonance imaging in confirming eligibility for active surveillance for men with prostate cancer , 2013, Cancer.
[48] C. Ogden,et al. A multi-centre prospective development study evaluating focal therapy using high intensity focused ultrasound for localised prostate cancer: The INDEX study , 2013, Contemporary clinical trials.
[49] Dean C. Barratt,et al. Fully automated prostate magnetic resonance imaging and transrectal ultrasound fusion via a probabilistic registration metric , 2013, Medical Imaging.
[50] Thomas Hambrock,et al. Simulated required accuracy of image registration tools for targeting high-grade cancer components with prostate biopsies , 2013, European Radiology.
[51] Zeike A. Taylor,et al. MR to ultrasound registration for image-guided prostate interventions , 2012, Medical Image Anal..
[52] David J. Hawkes,et al. Deformable Vessel-Based Registration Using Landmark-Guided Coherent Point Drift , 2010, MIAR.
[53] Jasjit S. Suri,et al. MRI-ultrasound registration for targeted prostate biopsy , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.