Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy

[1]  C A T van den Berg,et al.  Fast contour propagation for MR‐guided prostate radiotherapy using convolutional neural networks , 2019, Medical physics.

[2]  S. Senan,et al.  A prospective single-arm phase II study of stereotactic magnetic-resonance-guided adaptive radiotherapy for prostate cancer: Early toxicity results. , 2019, International journal of radiation oncology, biology, physics.

[3]  Yang Lei,et al.  Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[4]  Harini Veeraraghavan,et al.  Deep learning-based auto-segmentation of targets and organs-at-risk for magnetic resonance imaging only planning of prostate radiotherapy , 2019, Physics and imaging in radiation oncology.

[5]  Indrin J Chetty,et al.  Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN). , 2019, International journal of radiation oncology, biology, physics.

[6]  Paul Keall,et al.  See, Think, and Act: Real-Time Adaptive Radiotherapy. , 2019, Seminars in radiation oncology.

[7]  Carlos E Cardenas,et al.  Advances in Auto-Segmentation. , 2019, Seminars in radiation oncology.

[8]  Rob H.N. Tijssen,et al.  Feasibility of stereotactic radiotherapy using a 1.5 T MR-linac: Multi-fraction treatment of pelvic lymph node oligometastases. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[9]  Li Wang,et al.  STRAINet: Spatially Varying sTochastic Residual AdversarIal Networks for MRI Pelvic Organ Segmentation , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Tian Liu,et al.  Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation , 2019, Medical physics.

[11]  Berkman Sahiner,et al.  Deep learning in medical imaging and radiation therapy. , 2018, Medical physics.

[12]  D. Rangaraj,et al.  Transmission detectors are safe and the future for patient-specific QA in radiation therapy. , 2018, Medical physics.

[13]  L G W Kerkmeijer,et al.  Magnetic Resonance Imaging only Workflow for Radiotherapy Simulation and Planning in Prostate Cancer. , 2018, Clinical oncology (Royal College of Radiologists (Great Britain)).

[14]  Yonggang Lu,et al.  A novel MRI segmentation method using CNN‐based correction network for MRI‐guided adaptive radiotherapy , 2018, Medical physics.

[15]  Geraint Rees,et al.  Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy , 2018, ArXiv.

[16]  Gina Brown,et al.  The rectosigmoid problem. , 2018, Surgical oncology.

[17]  Rob H.N. Tijssen,et al.  Feasibility of magnetic resonance imaging-only rectum radiotherapy with a commercial synthetic computed tomography generation solution , 2018, Physics and imaging in radiation oncology.

[18]  Alex Lallement,et al.  Survey on deep learning for radiotherapy , 2018, Comput. Biol. Medicine.

[19]  Steve B. Jiang,et al.  Fully automated organ segmentation in male pelvic CT images , 2018, Physics in medicine and biology.

[20]  Dinggang Shen,et al.  Semi-supervised learning for pelvic MR image segmentation based on multi-task residual fully convolutional networks , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[21]  Bradley R Pieters,et al.  ESTRO ACROP consensus guideline on CT- and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer. , 2018, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[22]  Steve B. Jiang,et al.  Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning , 2018, Biomedical Physics & Engineering Express.

[23]  Peter R Seevinck,et al.  Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy , 2018, Physics in medicine and biology.

[24]  Dean C. Barratt,et al.  Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks , 2018, IEEE Transactions on Medical Imaging.

[25]  Paul Aljabar,et al.  Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[26]  Kuo Men,et al.  Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks , 2017, Medical physics.

[27]  A N T J Kotte,et al.  First patients treated with a 1.5 T MRI-Linac: clinical proof of concept of a high-precision, high-field MRI guided radiotherapy treatment , 2017, Physics in Medicine and Biology.

[28]  Expert Panel on Neurologic Imaging.  ACR Appropriateness Criteria® Prostate Cancer—Pretreatment Detection, Surveillance, and Staging , 2017 .

[29]  Fabio Martínez,et al.  A multiresolution prostate representation for automatic segmentation in magnetic resonance images , 2017, Medical physics.

[30]  T. Nyholm,et al.  A review of substitute CT generation for MRI-only radiation therapy , 2017, Radiation oncology.

[31]  Peter R Seevinck,et al.  Quantification of confounding factors in MRI-based dose calculations as applied to prostate IMRT , 2017, Physics in medicine and biology.

[32]  Adam T Froemming,et al.  ACR Appropriateness Criteria® Prostate Cancer-Pretreatment Detection, Surveillance, and Staging. , 2017, Journal of the American College of Radiology : JACR.

[33]  Elisa Sayrol Clols,et al.  3D convolutional neural networks for brain tumor segmentation , 2016 .

[34]  Konstantinos Kamnitsas,et al.  DeepMedic for Brain Tumor Segmentation , 2016, BrainLes@MICCAI.

[35]  Stéphane Supiot,et al.  Comparison of Automated Atlas-Based Segmentation Software for Postoperative Prostate Cancer Radiotherapy , 2016, Front. Oncol..

[36]  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).

[37]  Eric Achten,et al.  Optimal number of atlases and label fusion for automatic multi-atlas-based brachial plexus contouring in radiotherapy treatment planning , 2016, Radiation Oncology.

[38]  Heinz Handels,et al.  Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries , 2017, Lecture Notes in Computer Science.

[39]  Jurgen Fripp,et al.  Automatic Substitute Computed Tomography Generation and Contouring for Magnetic Resonance Imaging (MRI)-Alone External Beam Radiation Therapy From Standard MRI Sequences. , 2015, International journal of radiation oncology, biology, physics.

[40]  Maria A Schmidt,et al.  Radiotherapy planning using MRI , 2015, Physics in medicine and biology.

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

[42]  Stuart Crozier,et al.  The Australian magnetic resonance imaging-linac program. , 2014, Seminars in radiation oncology.

[43]  Karin Haustermans,et al.  The value of magnetic resonance imaging for radiotherapy planning. , 2014, Seminars in radiation oncology.

[44]  G. Sharp,et al.  Vision 20/20: perspectives on automated image segmentation for radiotherapy. , 2014, Medical physics.

[45]  Qiu-Hua Lin,et al.  Machine Learning in Medical Imaging , 2013, Lecture Notes in Computer Science.

[46]  Xiao Han.  Learning-Boosted Label Fusion for Multi-atlas Auto-Segmentation , 2013, MLMI.

[47]  A. Lomax,et al.  Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer , 2012, Radiation oncology.

[48]  Issam El Naqa,et al.  Pelvic normal tissue contouring guidelines for radiation therapy: a Radiation Therapy Oncology Group consensus panel atlas. , 2012, International journal of radiation oncology, biology, physics.

[49]  H. Eggers,et al.  Dual‐echo Dixon imaging with flexible choice of echo times , 2011, Magnetic resonance in medicine.

[50]  Xiao Han,et al.  GPU-accelerated, gradient-free MI deformable registration for atlas-based MR brain image segmentation , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[51]  B. Fallone,et al.  First MR images obtained during megavoltage photon irradiation from a prototype integrated linac-MR system. , 2009, Medical physics.

[52]  C. Njeh,et al.  Tumor delineation: The weakest link in the search for accuracy in radiotherapy , 2008, Journal of medical physics.

[53]  Maximilien Vermandel,et al.  Automatic segmentation of pelvic structures from magnetic resonance images for prostate cancer radiotherapy. , 2007, International journal of radiation oncology, biology, physics.

[54]  Michel Bolla,et al.  Guidelines for primary radiotherapy of patients with prostate cancer. , 2006, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[55]  Sasa Mutic,et al.  A Device for Realtime 3D Image-Guided IMRT , 2005 .

[56]  S. Singer,et al.  Influence of site on the therapeutic ratio of adjuvant radiotherapy in soft-tissue sarcoma of the extremity. , 2005, International journal of radiation oncology, biology, physics.

[57]  Filip Claus,et al.  Interobserver Delineation Variation Using CT versus Combined CT + MRI in Intensity–Modulated Radiotherapy for Prostate Cancer , 2005, Strahlentherapie und Onkologie.

[58]  Vladimir Pekar,et al.  Automated model-based organ delineation for radiotherapy planning in prostatic region. , 2004, International journal of radiation oncology, biology, physics.

[59]  A N T J Kotte,et al.  Integrating a MRI scanner with a 6 MV radiotherapy accelerator: dose deposition in a transverse magnetic field. , 2004, Physics in medicine and biology.

[60]  Lisa Barbera,et al.  Does delay in starting treatment affect the outcomes of radiotherapy? A systematic review. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[61]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[62]  E Bellon,et al.  The contribution of magnetic resonance imaging to the three-dimensional treatment planning of localized prostate cancer. , 1999, International journal of radiation oncology, biology, physics.

[63]  M van Herk,et al.  Definition of the prostate in CT and MRI: a multi-observer study. , 1999, International journal of radiation oncology, biology, physics.

[64]  J. Hintze,et al.  Violin plots : A box plot-density trace synergism , 1998 .

[65]  M. Beauchemin,et al.  On the Hausdorff Distance Used for the Evaluation of Segmentation Results , 1998 .

[66]  H. Hricak,et al.  Prostate volumes defined by magnetic resonance imaging and computerized tomographic scans for three-dimensional conformal radiotherapy. , 1996, International journal of radiation oncology, biology, physics.

[67]  W. T. Dixon.  Simple proton spectroscopic imaging. , 1984, Radiology.