A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy

[1]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[3]  W D Richard,et al.  Automated texture-based segmentation of ultrasound images of the prostate. , 1996, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[4]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[5]  R G Aarnink,et al.  Edge detection in prostatic ultrasound images using integrated edge maps. , 1998, Ultrasonics.

[6]  Mariano Alcañiz Raya,et al.  Outlining of the prostate using snakes with shape restrictions based on the wavelet transform , 1999, Pattern Recognit..

[7]  Yongmin Kim,et al.  Edge-guided boundary delineation in prostate ultrasound images , 2000, IEEE Transactions on Medical Imaging.

[8]  Yan Yu,et al.  Automatic segmentation of prostate boundaries in transrectal ultrasound (TRUS) imaging , 2002, SPIE Medical Imaging.

[9]  Dinggang Shen,et al.  Segmentation of prostate boundaries from ultrasound images using statistical shape model , 2003, IEEE Transactions on Medical Imaging.

[10]  Purang Abolmaesumi,et al.  Segmentation of prostate contours from ultrasound images , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Maximilien Vermandel,et al.  Segmentation of abdominal ultrasound images of the prostate using a priori information and an adapted noise filter. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[12]  Hamid R. Tizhoosh,et al.  Segmentation of prostate boundaries using regional contrast enhancement , 2005, IEEE International Conference on Image Processing 2005.

[13]  Aaron Fenster,et al.  Prostate boundary segmentation from ultrasound images using 2D active shape models: Optimisation and extension to 3D , 2006, Comput. Methods Programs Biomed..

[14]  H. C. Lim,et al.  Computerised prostate boundary estimation of ultrasound images using radial bas-relief method , 2006, Medical and Biological Engineering and Computing.

[15]  P. Fieguth,et al.  A Medical Texture Local Binary Pattern For TRUS Prostate Segmentation , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Amjad Zaim,et al.  An Energy-Based Segmentation of Prostate from Ultrasouind Images using Dot-Pattern Select Cells , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[17]  Amjad Zaim,et al.  Feature-Based Classification of Prostate Ultrasound Images using Multiwavelet and Kernel Support Vector Machines , 2007, 2007 International Joint Conference on Neural Networks.

[18]  Fernando Arámbula Cosío,et al.  Automatic initialization of an active shape model of the prostate , 2008, Medical Image Anal..

[19]  Jagath Samarabandu,et al.  Prostate Segmentation from 2-D Ultrasound Images Using Graph Cuts and Domain Knowledge , 2008, 2008 Canadian Conference on Computer and Robot Vision.

[20]  Fabrice Mériaudeau,et al.  Texture Guided Active Appearance Model Propagation for Prostate Segmentation , 2010, Prostate Cancer Imaging.

[21]  Sheng Xu,et al.  Discrete Deformable Model Guided by Partial Active Shape Model for TRUS Image Segmentation , 2010, IEEE Transactions on Biomedical Engineering.

[22]  Fabrice Mériaudeau,et al.  Statistical shape and texture model of quadrature phase information for prostate segmentation , 2011, International Journal of Computer Assisted Radiology and Surgery.

[23]  Fabrice Mériaudeau,et al.  Multiple Mean Models of Statistical Shape and Probability Priors for Automatic Prostate Segmentation , 2011, Prostate Cancer Imaging.

[24]  Septimiu E. Salcudean,et al.  Semi-automatic segmentation for prostate interventions , 2011, Medical Image Anal..

[25]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[26]  Desire Sidibé,et al.  A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images , 2012, Comput. Methods Programs Biomed..

[27]  Yiguang Liu,et al.  TRUS image segmentation with non-parametric kernel density estimation shape prior , 2013, Biomed. Signal Process. Control..

[28]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[29]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[31]  Purang Abolmaesumi,et al.  A Multi-Atlas-Based Segmentation Framework for Prostate Brachytherapy , 2015, IEEE Transactions on Medical Imaging.

[32]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[33]  Bingbing Liu,et al.  Robust Prostate Segmentation Using Intrinsic Properties of TRUS Images , 2015, IEEE Transactions on Medical Imaging.

[34]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Jürgen Schmidhuber,et al.  Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation , 2015, NIPS.

[36]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Pablo Lamata,et al.  Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation , 2016, RAMBO+HVSMR@MICCAI.

[38]  Purang Abolmaesumi,et al.  Learning-Based Multi-Label Segmentation of Transrectal Ultrasound Images for Prostate Brachytherapy , 2016, IEEE Transactions on Medical Imaging.

[39]  Christopher Joseph Pal,et al.  Delving Deeper into Convolutional Networks for Learning Video Representations , 2015, ICLR.

[40]  Martin Jägersand,et al.  Recurrent Fully Convolutional Networks for Video Segmentation , 2016, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[41]  U. Rajendra Acharya,et al.  Segmentation of prostate contours for automated diagnosis using ultrasound images: A survey , 2017, J. Comput. Sci..

[42]  L. Hooft,et al.  Comparing Three Different Techniques for Magnetic Resonance Imaging-targeted Prostate Biopsies: A Systematic Review of In-bore versus Magnetic Resonance Imaging-transrectal Ultrasound fusion versus Cognitive Registration. Is There a Preferred Technique? , 2017, European urology.

[43]  Purang Abolmaesumi,et al.  Clinical Target-Volume Delineation in Prostate Brachytherapy Using Residual Neural Networks , 2017, MICCAI.

[44]  Alan D. Lopez,et al.  Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study , 2017, JAMA oncology.

[45]  C. Tempany,et al.  The Current State of MR Imaging-targeted Biopsy Techniques for Detection of Prostate Cancer. , 2017, Radiology.

[46]  Martin Jägersand,et al.  Convolutional gated recurrent networks for video segmentation , 2016, 2017 IEEE International Conference on Image Processing (ICIP).

[47]  Xin Yang,et al.  Fine-Grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images , 2016, AAAI.

[48]  Dean C. Barratt,et al.  Automatic slice segmentation of intraoperative transrectal ultrasound images using convolutional neural networks , 2018, Medical Imaging.