Applications and limitations of machine learning in radiation oncology
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Eleanor Stride | Daniel Jarrett | Mark J Gooding | Katherine Vallis | E. Stride | Daniel Jarrett | M. Gooding | K. Vallis
[1] Mannudeep K. Kalra,et al. Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) , 2017, ArXiv.
[2] Andre Dekker,et al. Big Data in radiation therapy: challenges and opportunities , 2016, The British journal of radiology.
[3] Fang-Fang Yin,et al. Utilizing knowledge from prior plans in the evaluation of quality assurance , 2015, Physics in medicine and biology.
[4] Dinggang Shen,et al. Machine Learning in Medical Imaging , 2012, Lecture Notes in Computer Science.
[5] Martin J. Murphy,et al. Machine Learning in Radiation Oncology , 2015 .
[6] Xiao Han,et al. MR‐based synthetic CT generation using a deep convolutional neural network method , 2017, Medical physics.
[7] David A. Jaffray,et al. Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method , 2016, Physics in medicine and biology.
[8] A. Burgun,et al. Big Data and machine learning in radiation oncology: State of the art and future prospects. , 2016, Cancer letters.
[9] Su Ruan,et al. Medical Image Synthesis with Context-Aware Generative Adversarial Networks , 2016, MICCAI.
[10] Dorin Comaniciu,et al. An Artificial Agent for Robust Image Registration , 2016, AAAI.
[11] Paul Aljabar,et al. Comparative evaluation of autocontouring in clinical practice: A practical method using the Turing test , 2018, Medical physics.
[12] 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.
[13] Martha M. Matuszak,et al. Using a Knowledge-Based Planning Model for Quality Assurance of Liver Stereotactic Body Radiation Therapy Plans , 2016 .
[14] Sébastien Ourselin,et al. Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm. , 2015, Medical physics.
[15] Kianoush Nazarpour,et al. Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications. , 2016, Medical engineering & physics.
[16] Steve B. Jiang,et al. A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning , 2017, Scientific Reports.
[17] Benjamin E Nelms,et al. Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors. , 2011, Medical physics.
[18] Jong Chul Ye,et al. A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction , 2016, Medical physics.
[19] Satomi Shiraishi,et al. Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy. , 2015, Medical physics.
[20] T Kadir,et al. TU-AB-202-10: How Effective Are Current Atlas Selection Methods for Atlas-Based Auto-Contouring in Radiotherapy Planning? , 2016, Medical physics.
[21] Bulat Ibragimov,et al. Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networks , 2017, Medical physics.
[22] 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.
[23] 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.
[24] Sheng Xu,et al. Adversarial Image Registration with Application for MR and TRUS Image Fusion , 2018, MLMI@MICCAI.
[25] Martin J. Murphy. Artificial Neural Networks to Emulate and Compensate Breathing Motion During Radiation Therapy , 2015 .
[26] Carsten Brink,et al. Automatic planning of head and neck treatment plans , 2016, Journal of applied clinical medical physics.
[27] Alex Lallement,et al. Survey on deep learning for radiotherapy , 2018, Comput. Biol. Medicine.
[28] Gilmer Valdes,et al. Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[29] Issam El Naqa,et al. The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy , 2018, Front. Oncol..
[30] E W Korevaar,et al. An automated, quantitative, and case-specific evaluation of deformable image registration in computed tomography images , 2018, Physics in medicine and biology.
[31] Daniel Rueckert,et al. Structured Decision Forests for Multi-modal Ultrasound Image Registration , 2015, MICCAI.
[32] Frank Lohr,et al. Expert system classifier for adaptive radiation therapy in prostate cancer , 2017, Australasian Physical & Engineering Sciences in Medicine.
[33] Jen-Tzung Chien,et al. Deep reinforcement learning for automated radiation adaptation in lung cancer , 2017, Medical physics.
[34] Timothy C. Y. Chan,et al. Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks , 2018, MLHC.
[35] S. Ourselin,et al. Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning , 2017, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[36] Steve B. Jiang,et al. The management of imaging dose during image-guided radiotherapy: report of the AAPM Task Group 75. , 2007, Medical physics.
[37] Elena Gallio,et al. Evaluation of a commercial automatic treatment planning system for liver stereotactic body radiation therapy treatments. , 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[38] Shunxing Bao,et al. Fully convolutional neural networks improve abdominal organ segmentation , 2018, Medical Imaging.
[39] Clifton D Fuller,et al. Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function. , 2018, International journal of radiation oncology, biology, physics.
[40] So-Yeon Park,et al. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors , 2016, Physics in medicine and biology.
[41] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[42] Bernhard Schölkopf,et al. Learning similarity measure for multi-modal 3D image registration , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[43] K. Brock,et al. Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132 , 2017, Medical physics.
[44] Nikos Komodakis,et al. A Deep Metric for Multimodal Registration , 2016, MICCAI.
[45] M. Wendling,et al. Automated IMRT planning in Pinnacle , 2017, Strahlentherapie und Onkologie.
[46] Weigang Hu,et al. Is it possible for knowledge-based planning to improve intensity modulated radiation therapy plan quality for planners with different planning experiences in left-sided breast cancer patients? , 2017, Radiation Oncology.
[47] Jin Liu,et al. Discriminative Prior - Prior Image Constrained Compressed Sensing Reconstruction for Low-Dose CT Imaging , 2017, Scientific Reports.
[48] Satomi Shiraishi,et al. Fully automated, comprehensive knowledge-based planning for stereotactic radiosurgery: Preclinical validation through blinded physician review. , 2017, Practical radiation oncology.
[49] Timothy D. Solberg,et al. Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs , 2018, Front. Oncol..
[50] Thomas G. Purdie,et al. Groupwise Conditional Random Forests for Automatic Shape Classification and Contour Quality Assessment in Radiotherapy Planning , 2013, IEEE Transactions on Medical Imaging.
[51] M Monz,et al. Pareto navigation—algorithmic foundation of interactive multi-criteria IMRT planning , 2008, Physics in medicine and biology.
[52] Jun Tan,et al. Knowledge-based prediction of plan quality metrics in intracranial stereotactic radiosurgery. , 2015, Medical physics.
[53] Higino Correia,et al. Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[54] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[55] Kanabu Nawa,et al. Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network , 2018, Cureus.
[56] Li Zhang,et al. Deep similarity learning for multimodal medical images , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..
[57] Jelmer M. Wolterink,et al. Deep MR to CT Synthesis Using Unpaired Data , 2017, SASHIMI@MICCAI.
[58] Marcel van Herk,et al. Errors and margins in radiotherapy. , 2004, Seminars in radiation oncology.
[59] Dimos Baltas,et al. Esophagus segmentation in CT via 3D fully convolutional neural network and random walk , 2017, Medical physics.
[60] Maximilien Vermandel,et al. Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context , 2015, International Journal of Computer Assisted Radiology and Surgery.
[61] F Lohr,et al. A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation. , 2016, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[62] Jong Chul Ye,et al. Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis , 2016, ArXiv.
[63] Steve B. Jiang,et al. Dose Prediction with U-net: A Feasibility Study for Predicting Dose Distributions from Contours using Deep Learning on Prostate IMRT Patients , 2017, ArXiv.
[64] G. Sharp,et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. , 2014, Medical physics.
[65] Ge Wang,et al. Deep learning methods to guide CT image reconstruction and reduce metal artifacts , 2017, Medical Imaging.
[66] Vorakarn Chanyavanich,et al. Knowledge-based IMRT treatment planning for prostate cancer , 2010, Medical physics.
[67] Fang Liu,et al. Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging , 2018, Medical physics.
[68] Feng Lin,et al. Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network , 2017, IEEE Transactions on Medical Imaging.
[69] Nikos Paragios,et al. Boosted metric learning for 3D multi-modal deformable registration , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[70] Morteza Mardani,et al. Deep-Learning Based Prediction of Achievable Dose for Personalizing Inverse Treatment Planning , 2016 .
[71] David I Thwaites,et al. Now you see it... Imaging in radiotherapy treatment planning and delivery. , 2007, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[72] R. K. Ten Haken,et al. Reinforcement Learning Strategies for Decision Making in Knowledge-Based Adaptive Radiation Therapy: Application in Liver Cancer , 2016 .
[73] Hengyong Yu,et al. Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography , 2017, IEEE Transactions on Medical Imaging.
[74] Quanzheng Li,et al. Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network , 2017, IEEE Transactions on Medical Imaging.
[75] M. Kessler. Image registration and data fusion in radiation therapy. , 2006, The British journal of radiology.
[76] Gilmer Valdes,et al. An unsupervised convolutional neural network-based algorithm for deformable image registration , 2018, Physics in medicine and biology.
[77] Gilmer Valdes,et al. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? , 2018, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[78] Marc Niethammer,et al. Quicksilver: Fast predictive image registration – A deep learning approach , 2017, NeuroImage.
[79] Timothy D. Solberg,et al. Deep nets vs expert designed features in medical physics: An IMRT QA case study , 2018, Medical physics.
[80] Richard Speight,et al. Automated, reference-free local error assessment of multimodal deformable image registration for radiotherapy in the head and neck. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[81] David L Craft,et al. Approximating convex pareto surfaces in multiobjective radiotherapy planning. , 2006, Medical physics.
[82] Mert R. Sabuncu,et al. Multi-atlas segmentation of biomedical images: A survey , 2014, Medical Image Anal..
[83] Yaozong Gao,et al. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching , 2016, IEEE Transactions on Medical Imaging.
[84] Yaozong Gao,et al. Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy , 2016, MICCAI.
[85] T. Roques,et al. Patient selection and radiotherapy volume definition - can we improve the weakest links in the treatment chain? , 2014, Clinical oncology (Royal College of Radiologists (Great Britain)).
[86] Margie Hunt,et al. Dosimetric and workflow evaluation of first commercial synthetic CT software for clinical use in pelvis , 2017, Physics in medicine and biology.
[87] Timothy D. Solberg,et al. IMRT QA using machine learning: A multi‐institutional validation , 2017, Journal of applied clinical medical physics.
[88] C. Njeh,et al. Tumor delineation: The weakest link in the search for accuracy in radiotherapy , 2008, Journal of medical physics.
[89] Max Dahele,et al. Can knowledge-based DVH predictions be used for automated, individualized quality assurance of radiotherapy treatment plans? , 2015, Radiation Oncology.
[90] Tao Zhang,et al. Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning. , 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[91] Xun Jia,et al. A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning. , 2014, Medical physics.
[92] J. Conway,et al. CT simulation for radiotherapy treatment planning. , 2002, The British journal of radiology.
[93] Christopher Kurz,et al. [OA127] Cone-beam CT intensity correction for adaptive radiotherapy of the prostate using deep learning , 2018 .
[94] P. Noël,et al. The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence , 2018, European radiology.