The DOI matches its Crossref record; 230 of the 237 references it cites were found in a registry or an index.
Survey on deep learning for radiotherapy
Abstract
More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“They can also be used to reduce the gadolinium dose in contrast-enhanced brain MRI by an order of magnitude [88] without significant reduction in image quality.”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [88]
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012
Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D. M., Forman, D., & Bray, F. (2015) · International Journal of Cancer · doi:10.1002/ijc.29210 · Cited by 28,766
GAN(Generative Adversarial Nets)
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
(2016) · Cited by 11,679
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Computing Machinery and Intelligence (1950)
Turing, A. M. (2004) · doi:10.1093/oso/9780198250791.003.0017 · Cited by 6,120
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
Some Studies in Machine Learning Using the Game of Checkers
Samuel, A. L. (1959) · IBM Journal of Research and Development · doi:10.1147/rd.33.0210 · Cited by 4,443
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498
Some Philosophical Problems from the Standpoint of Artificial Intelligence
McCarthy, J., & Hayes, P. J. (1981) · Elsevier eBooks · doi:10.1016/b978-0-934613-03-3.50033-7 · Cited by 3,296
Brain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., & Clune, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298640 · Cited by 3,258
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076
(2013) · Springer eBooks · Cited by 2,625
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412
Current Methods in Medical Image Segmentation
Pham, D. L., Xu, C., & Prince, J. L. (2000) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.2.1.315 · Cited by 2,352
Deep Learning for Health Informatics
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636665 · Cited by 2,000
Computer-aided diagnosis in medical imaging: historical review, current status and future potential
Doi, K. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.02.002 · Cited by 1,855
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2767063 · Cited by 1,688
Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra, M. K., Zhang, Y., Sun, L., & Wang, G. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2827462 · Cited by 1,630
Deep Learning in Medical Imaging: General Overview
Lee, J., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017) · Korean Journal of Radiology · doi:10.3348/kjr.2017.18.4.570 · Cited by 1,328
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
Uzuner, Ö., South, B. R., Shen, S., & DuVall, S. L. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000203 · Cited by 1,292
(2011) · Cited by 1,258
Foundations of Machine Learning
(2012) · The MIT Press eBooks · doi:10.7551/mitpress/8291.003.0006 · Cited by 1,151
Overview of deep learning in medical imaging
Suzuki, K. (2017) · Radiological Physics and Technology · doi:10.1007/s12194-017-0406-5 · Cited by 1,131
Deep learning for brain MRI segmentation: state of the art and future directions
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122
Naqa, I. E., & Murphy, M. J. (2015) · Springer eBooks · doi:10.1007/978-3-319-18305-3_1 · Cited by 913
MR-based synthetic CT generation using a deep convolutional neural network method
Han, X. (2017) · Medical Physics · doi:10.1002/mp.12155 · Cited by 890
Brock, K. K., Mutic, S., McNutt, T. R., Li, H., & Kessler, M. L. (2017) · Medical Physics · doi:10.1002/mp.12256 · Cited by 881
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network
Moeskops, P., Viergever, M. A., Mendrik, A. M., de Vries, L. S., Benders, M. J. N. L., & Isgum, I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2548501 · Cited by 861
The logic theory machine-A complex information processing system
Newell, A., & Simon, H. (1956) · IEEE Transactions on Information Theory · doi:10.1109/tit.1956.1056797 · Cited by 846
Yan, D., Vicini, F. A., Wong, J. W., & Martinez, A. A. (1997) · Physics in Medicine and Biology · doi:10.1088/0031-9155/42/1/008 · Cited by 767
Review of MRI-based brain tumor image segmentation using deep learning methods
Işın, A., Direkoğlu, C., & Şah, M. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.09.407 · Cited by 766
aLow-dose CT via convolutional neural network
Chen, H., Zhang, Y., Zhang, W., Liao, P., Li, K., Zhou, J., & Wang, G. (2017) · Biomedical Optics Express · doi:10.1364/boe.8.000679 · Cited by 755
Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance.
Yuan, Y., Chao, M., & Lo, Y.-C. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2695227 · Cited by 687
Deep MR to CT Synthesis Using Unpaired Data
Wolterink, J. M., Dinkla, A. M., Savenije, M. H. F., Seevinck, P. R., van den Berg, C. A. T., & Išgum, I. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68127-6_2 · Cited by 661
Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation
Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2482920 · Cited by 608
Chen, Y.-J. Y.-J., Hua, K., Hsu, C.-H., Cheng, W.-H., & Hidayati, S. C. (2015) · OncoTargets and Therapy · doi:10.2147/ott.s80733 · Cited by 563
A Review of Feature Reduction Techniques in Neuroimaging
Mwangi, B., Tian, T. S., & Soares, J. C. (2014) · Neuroinformatics · doi:10.1007/s12021-013-9204-3 · Cited by 559
Image-guided radiotherapy: from current concept to future perspectives
Jaffray, D. A. (2012) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2012.194 · Cited by 515
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Kamnitsas, K., Bai, W., Ferrante, E., McDonagh, S., Sinclair, M. D., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., Rueckert, D., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75238-9_38 · Cited by 506
Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networks
Ibragimov, B., & Xing, L. (2017) · Medical Physics · doi:10.1002/mp.12045 · Cited by 502
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
Bernal, J., Kushibar, K., Asfaw, D. S., Valverde, S., Oliver, A., Martí, R., & Lladó, X. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.08.008 · Cited by 458
Medical image retrieval using deep convolutional neural network
Qayyum, A., Anwar, S. M., Awais, M., & Majid, M. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.05.025 · Cited by 433
Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images
Song, Q., Zhao, L., Luo, X., & Dou, X. (2017) · Journal of Healthcare Engineering · doi:10.1155/2017/8314740 · Cited by 422
AI: the tumultuous history of the search for artificial intelligence
(1993) · Choice Reviews Online · doi:10.5860/choice.31-1555 · Cited by 420
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
Dolz, J., Desrosiers, C., & Ayed, I. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.039 · Cited by 415
Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound
Milletarì, F., Ahmadi, S., Kroll, C., Plate, A., Rozanski, V. E., Maiostre, J., Levin, J., Dietrich, O., Ertl-Wagner, B. B., Bötzel, K., et al. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.04.002 · Cited by 406
Lung Nodule Classification Using Deep Features in CT Images
Kumar, D., Wong, A., & Clausi, D. A. (2015) · 2015 12th Conference on Computer and Robot Vision · doi:10.1109/crv.2015.25 · Cited by 398
Artificial intelligence and natural man
Brady, J. M. (1978) · Artificial Intelligence · doi:10.1016/0004-3702(78)90003-6 · Cited by 395
(1977) · Cited by 395
Vision 20/20: Perspectives on automated image segmentation for radiotherapy
Sharp, G. C., Fritscher, K. D., Pekar, V., Peroni, M., Shusharina, N., Veeraraghavan, H., & Yang, J. (2014) · Medical Physics · doi:10.1118/1.4871620 · Cited by 377
A review of substitute CT generation for MRI-only radiation therapy
Edmund, J., & Nyholm, T. (2017) · Radiation Oncology · doi:10.1186/s13014-016-0747-y · Cited by 377
Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer
Lustberg, T., van Soest, J., Gooding, M. J., Peressutti, D., Aljabar, P., van der Stoep, J., van Elmpt, W., & Dekker, A. L. A. J. (2017) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2017.11.012 · Cited by 377
The Recurrent Temporal Restricted Boltzmann Machine
Sutskever, I., Hinton, G. E., & Taylor, G. W. (2008) · Cited by 370
Radiotherapy planning using MRI
Schmidt, M. A., & Payne, G. S. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/22/r323 · Cited by 358
Big Data and machine learning in radiation oncology: State of the art and future prospects
Bibault, J.-E., Giraud, P., & Burgun, A. (2016) · Cancer Letters · doi:10.1016/j.canlet.2016.05.033 · Cited by 346
DeepNAT: Deep convolutional neural network for segmenting neuroanatomy
Wachinger, C., Reuter, M., & Klein, T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.035 · Cited by 346
DeepMedic for Brain Tumor Segmentation
Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A. V., Criminisi, A., Rueckert, D., & Glocker, B. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-55524-9_14 · Cited by 346
Uses of Electronic Health Records for Public Health Surveillance to Advance Public Health
Birkhead, G. S., Klompas, M., & Shah, N. R. (2015) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031914-122747 · Cited by 345
A survey of medical image registration – under review
Viergever, M. A., Maintz, J. B. A., Klein, S., Murphy, K., Staring, M., & Pluim, J. P. W. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.06.030 · Cited by 318
Men, K., Dai, J., & Li, Y. (2017) · Medical Physics · doi:10.1002/mp.12602 · Cited by 305
Trebeschi, S., van Griethuysen, J. J. M., Lambregts, D. M. J., Lahaye, M. J., Parmar, C., Bakers, F. C. H., Peters, N. H. G. M., Beets‐Tan, R. G. H., & Aerts, H. J. W. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-05728-9 · Cited by 298
Automatic 3D liver location and segmentation via convolutional neural network and graph cut
Lu, F., Wu, F., Hu, P., Peng, Z., & Kong, D. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1467-3 · Cited by 284
Guorong Wu, A., Kim, M., Wang, Q., Munsell, B. C., & Shen, D. G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2496253 · Cited by 283
Christ, P. F., Ettlinger, F., Grün, F., Elshaer, M. E. A., Lipková, J., Schlecht, S. J., Ahmaddy, F., Tatavarty, S., Bickel, M., Bilic, P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05970 · Cited by 278
Wang, H., Zhou, Z., Li, Y., Chen, Z., Lu, P., Wang, W., Liu, W., & Yu, L. (2017) · EJNMMI Research · doi:10.1186/s13550-017-0260-9 · Cited by 276
Online Adaptive Radiation Therapy
Lim‐Reinders, S., Keller, B. M., Al-Ward, S. M., Sahgal, A., & Kim, A. (2017) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2017.04.023 · Cited by 271
Improving Dermoscopic Image Segmentation With Enhanced Convolutional-Deconvolutional Networks
Yuan, Y., & Lo, Y. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2787487 · Cited by 268
Charron, O., Lallement, A., Jarnet, D., Noblet, V., Clavier, J.-B., & Meyer, P. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.02.004 · Cited by 268
Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching
Guo, Y., Gao, Y., & Shen, D. G. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2508280 · Cited by 258
Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks
Nie, D., Cao, X., Gao, Y., Wang, L., & Shen, D. G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_18 · Cited by 257
Deep reinforcement learning for automated radiation adaptation in lung cancer
Tseng, H., Luo, Y., Cui, S., Chien, J., Haken, R. K. T., & Naqa, I. E. (2017) · Medical Physics · doi:10.1002/mp.12625 · Cited by 245
Fully Convolutional Network for Liver Segmentation and Lesions Detection
Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M. M., & Greenspan, H. K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_9 · Cited by 241
Automatic 3D pulmonary nodule detection in CT images: A survey
Valente, I. R. S., Cortez, P. C., Neto, E. C., Soares, J. M., de Albuquerque, V. H. C., & Tavares, J. M. R. S. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.10.006 · Cited by 237
MRI-only treatment planning: benefits and challenges
Owrangi, A., Greer, P. B., & Glide‐Hurst, C. K. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aaaca4 · Cited by 236
Hsu, S.-H., Cao, Y., Huang, K. C., Feng, M. U.-S., & Balter, J. M. (2013) · Physics in Medicine and Biology · doi:10.1088/0031-9155/58/23/8419 · Cited by 232
Teramoto, A., Fujita, H., Yamamuro, O., & Tamaki, T. (2016) · Medical Physics · doi:10.1118/1.4948498 · Cited by 229
Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective
Kang, J., Schwartz, R. S., Flíckinger, J. C., & Beriwal, S. (2015) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2015.07.2286 · Cited by 227
Sun, W., Zheng, B., & Qian, W. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.04.006 · Cited by 227
Using deep learning to segment breast and fibroglandular tissue in MRI volumes
Dalmış, M. U., Litjens, G. J. S., Holland, K., Setio, A. A. A., Mann, R. M., Karssemeijer, N., & Gubern‐Mérida, A. (2016) · Medical Physics · doi:10.1002/mp.12079 · Cited by 211
Men, K., Chen, X., Zhang, Y., Zhang, T., Dai, J., Yi, J., & Li, Y. (2017) · Frontiers in Oncology · doi:10.3389/fonc.2017.00315 · Cited by 201
McIntosh, C., Welch, M. L., McNiven, A., Jaffray, D. A., & Purdie, T. G. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa71f8 · Cited by 194
Hu, P., Wu, F., Peng, J., Bao, Y., Chen, F., & Kong, D.-X. (2016) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1501-5 · Cited by 194
Zhang, J., Liu, M., & Shen, D. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2721106 · Cited by 192
Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image
Xiang, L., Wang, Q., Nie, D., Zhang, L., Jin, X., Qiao, Y., & Shen, D. G. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.03.011 · Cited by 183
Recent advances in video-based human action recognition using deep learning: A review
Wu, D., Sharma, N., & Blumenstein, M. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966210 · Cited by 173
Liu, Y., Stojadinović, S., Hrycushko, B. A., Wardak, Z., Lau, S. K. M., Lu, W., Yan, Y., Jiang, S. B., Zhen, X., Timmerman, R. D., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185844 · Cited by 170
An Artificial Agent for Anatomical Landmark Detection in Medical Images
Ghesu, F. C., Georgescu, B., Mansi, T., Neumann, D., Joachim Hornegger, A., & Comaniciu, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_27 · Cited by 161
Giantsoudi, D., Man, B. D., Verburg, J., Trofimov, A. V., Jin, Y., Wang, G., Gjesteby, L., & Paganetti, H. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa5293 · Cited by 157
Zhou, T., Han, G., Li, B. N., Lin, Z., Ciaccio, E. J., Green, P. H., & Qin, J. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.03.031 · Cited by 157
The Role of Imaging in Radiation Therapy Planning: Past, Present, and Future
Pereira, G. C., Traughber, M. S., & Muzic, R. F. (2014) · BioMed Research International · doi:10.1155/2014/231090 · Cited by 152
3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data
Zheng, Y., Liu, D., Georgescu, B., Nguyen, H. V., & Comaniciu, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_69 · Cited by 151
Nonlinear Systems Identification Using Deep Dynamic Neural NetworksPreprint
Ogunmolu, O., Gu, X., Jiang, S. B., & Gans, N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.01439 · Cited by 151
Cardenas, C. E., McCarroll, R. E., Court, L. E., Elgohari, B., Elhalawani, H. M., Fuller, C. D., Kamal, M. J., Meheissen, M. A. M., Mohamed, A., Rao, A., et al. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.01.114 · Cited by 150
A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography
Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hashoul, S., Ben‐Ari, R., & Barkan, E. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_21 · Cited by 149
Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network
Wu, Y., Min, J., Jianbo, L., & Xu, H. (2015) · Studies in health technology and informatics · doi:10.3233/978-1-61499-564-7-624 · Cited by 148
Interleaved 3D‐ CNN s for joint segmentation of small‐volume structures in head and neck CT images
Ren, X., Xiang, L., Nie, D., Shao, Y., Zhang, H., Shen, D. G., & Wang, Q. (2018) · Medical Physics · doi:10.1002/mp.12837 · Cited by 148
The first MICCAI challenge on PET tumor segmentation
Hatt, M., Laurent, B., Ouahabi, A., Fayad, H. J., Tan, S., Li, L., Lü, W., Jaouen, V., Tauber, C., Czakon, J., et al. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.12.007 · Cited by 147
MRI-guided prostate adaptive radiotherapy – A systematic review
McPartlin, A. J., Li, X. A., Kershaw, L. E., van der Heide, U. A., Kerkmeijer, L. G. W., Lawton, C. A., Mahmood, U. A., Pos, F. J., van As, N. J., van Herk, M., et al. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.04.014 · Cited by 146
Deep Learning for Medical Image Analysis
Şenaras, Ç., & Gürcan, M. N. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_27_18 · Cited by 140
Jochems, A., Deist, T., Naqa, I. E., Kessler, M., Mayo, C., Reeves, J., Jolly, S., Matuszak, M., Haken, R. T., van Soest, J., et al. (2017) · International Journal of Radiation Oncology, Biology, Physics · doi:10.1016/j.ijrobp.2017.04.021 · Cited by 137
LUNGx Challenge for computerized lung nodule classification
Kirby, J., Armato, S. G., Drukker, K., Li, F., Hadjiiski, L. M., Tourassi, G. D., Clarke, L. P., Engelmann, R. M., Giger, M. L., Redmond, G., et al. (2016) · Journal of medical imaging · doi:10.1117/1.jmi.3.4.044506 · Cited by 131
Image quality transfer and applications in diffusion MRI
Alexander, D. C., Zikic, D., Ghosh, A., Tanno, R., Wottschel, V., Zhang, J., Kaden, E., Dyrby, T. B., Sotiropoulos, S. N., Zhang, H. G., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.089 · Cited by 131
Learned vs. Hand-Crafted Features for Pedestrian Gender Recognition
Antipov, G., Berrani, S.-A., Ruchaud, N., & Dugelay, J. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806332 · Cited by 129
Fast Predictive Image Registration
Yang, X., Kwitt, R., & Niethammer, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46976-8_6 · Cited by 127
Lung Nodule Detection via Deep Reinforcement Learning
Ali, I. M., Hart, G. R., Gunabushanam, G., Liang, Y., Muhammad, W., Nartowt, B. J., Kane, M. J., Ma, X., & Deng, J. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00108 · Cited by 125
Kushibar, K., Valverde, S., González-Villà, S., Bernal, J., Cabezas, M., Oliver, A., & Lladó, X. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.06.006 · Cited by 121
Bahrami, K., Shi, F., Rekik, I., & Shen, D. G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_5 · Cited by 117
Deep Learning in Medical Image Analysis
Zhang, Y., Górriz, J. M., & Dong, Z. (2021) · MDPI eBooks · doi:10.3390/books978-3-0365-1470-3 · Cited by 117
A survey on computational intelligence approaches for predictive modeling in prostate cancer
Cosma, G., Brown, D. J., Archer, M., Khan, M. A., & Pockley, A. G. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.11.006 · Cited by 116
Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs
Feng, M. U.-S., Valdés, G., Dixit, N., & Solberg, T. D. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00110 · Cited by 116
Dean, J., Wong, K. H., Welsh, L. C., Jones, A.-B., Schick, U., Newbold, K. L., Bhide, S. A., Harrington, K. J., Nutting, C. M., & Gulliford, S. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.05.015 · Cited by 115
Forsberg, D., Sjöblom, E., & Sunshine, J. L. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-9945-x · Cited by 113
Dilated FCN for Multi-Agent 2D/3D Medical Image Registration
Miao, S., Piat, S., Fischer, P., Tuysuzoglu, A., Mewes, P., Mansi, T., & Liao, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11576 · Cited by 113
Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data
Anirudh, R., Thiagarajan, J. J., Bremer, P., & Kim, H. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2214876 · Cited by 110
LeCun, Y. (1985) · Cited by 109
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Crimi, A., Menze, B. H., Maier, O., Reyes, M., & Heinz Handels, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30858-6 · Cited by 108
Brain tumor segmentation using holistically nested neural networks in MRI images
Zhuge, Y., Krauze, A. V., Ning, H., Cheng, J. Y., Arora, B. C., Camphausen, K. A., & Miller, R. W. (2017) · Medical Physics · doi:10.1002/mp.12481 · Cited by 108
O'Shea, T. P., Bamber, J. C., Fontanarosa, D., van der Meer, S., Verhaegen, F., & Harris, E. (2016) · Physics in Medicine and Biology · doi:10.1088/0031-9155/61/8/r90 · Cited by 107
Qin, W., Wu, J., Han, F., Yuan, Y., Zhao, W., Ibragimov, B., Gu, J., & Xing, L. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aabd19 · Cited by 106
Kooi, T., van Ginneken, B., Karssemeijer, N., & den Heeten, A. (2017) · Medical Physics · doi:10.1002/mp.12110 · Cited by 105
Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural NetworkPreprint
Zhang, H., Li, L., Qiao, K., Wang, L., Yan, B., Li, L., & Hu, G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.08707 · Cited by 104
Deep Learning Trends for Focal Brain Pathology Segmentation in MRI
Havaei, M., Guizard, N., Larochelle, H., & Jodoin, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50478-0_6 · Cited by 103
Zhou, X., Ito, T., Takayama, R., Wang, S., Hara, T., & Fujita, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_12 · Cited by 101
Real-time 2D/3D registration via CNN regression
Miao, S., Wang, Z. J., Zheng, Y., & Liao, R. (2016) · 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493536 · Cited by 101
Fontanarosa, D., van der Meer, S., Bamber, J. C., Harris, E., O'Shea, T. P., & Verhaegen, F. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/3/r77 · Cited by 97
Colvill, E., Booth, J., Nill, S., Fast, M. F., Bedford, J. L., Oelfke, U., Nakamura, M., Poulsen, P. R., Worm, E. S., Hansen, R., et al. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.03.006 · Cited by 97
SLIDE: automatic spine level identification system using a deep convolutional neural network
Hetherington, J., Lessoway, V. A., Gunka, V. B., Abolmaesumi, P., & Rohling, R. N. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1575-8 · Cited by 97
Persson, E., Gustafsson, C. J., Nordström, F., Sohlin, M. C., Gunnlaugsson, A., Petruson, K. M., Rintelä, N., Hed, K., Blomqvist, L., Zackrisson, B., et al. (2017) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2017.06.006 · Cited by 96
Perspectives to Definition of Big Data: A Mapping Study and Discussion
Ylijoki, O., & Porras, J. (2016) · Journal of Innovation Management · doi:10.24840/2183-0606_004.001_0006 · Cited by 91
Ibragimov, B., Toesca, D. A. S., Chang, D. T., Koong, A. C., & Xing, L. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa9262 · Cited by 91
Banerjee, I., Crawley, A., Bhethanabotla, M., Daldrup-Link, H. E., & Rubin, D. L. (2017) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2017.05.002 · Cited by 90
Yuan, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.04540 · Cited by 90
Roth, H. R., Yao, J.-H., Lu, L., Stieger, J., Burns, J. E., & Summers, R. (2014) · arXiv.org · doi:10.1007/978-3-319-14148-0_1 · Cited by 89
Simulation-based optimization of radiotherapy: Agent-based modeling and reinforcement learning
Jalalimanesh, A., Haghighi, S. S., Ahmadi, A., & Soltani, M. (2016) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2016.05.008 · Cited by 87
Deep learning methods to guide CT image reconstruction and reduce metal artifacts
Gjesteby, L., Yang, Q., Xi, Y., Zhou, Y., Zhang, J., & Wang, G. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2254091 · Cited by 87
Multimodal image registration with deep context reinforcement learning
Ma, K., Wang, J., Singh, V., Tamersoy, B., Chang, Y.-J., Wimmer, A., & Chen, T. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66182-7_28 · Cited by 87
Cha, K. H., Hadjiiski, L. M., Samala, R. K., Chan, H., Cohan, R. H., Caoili, E. M., Paramagul, C., Alva, A. S., & Weizer, A. Z. (2016) · Tomography · doi:10.18383/j.tom.2016.00184 · Cited by 84
A convolutional neural network to filter artifacts in spectroscopic MRI
Gurbani, S. S., Schreibmann, E., Maudsley, A. A., Cordova, J. S., Soher, B. J., Poptani, H., Verma, G., Barker, P. B., Shim, H., & Cooper, L. A. D. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27166 · Cited by 84
Esophagus segmentation in CT via 3D fully convolutional neural network and random walk
Fechter, T., Adebahr, S., Baltas, D., Ayed, I. B., Desrosiers, C., & Dolz, J. (2017) · Medical Physics · doi:10.1002/mp.12593 · Cited by 83
Trullo, R., Petitjean, C., Ruan, S., Dubray, B. M., Nie, D., & Shen, D. G. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950685 · Cited by 81
Schubert, C., Waletzko, O., Weiß, C., Völzke, D., Toperim, S., Röser, A., Puccini, S., Piroth, M. D., Mehrens, C., Kueter, J.-D., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178034 · Cited by 79
Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies
Vivanti, R., Szeskin, A., Lev‐Cohain, N., Sosna, J., & Leo Joskowicz, B. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1660-z · Cited by 78
Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning
Park, S., Lee, S. J., Weiss, E., & Motai, Y. (2016) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2016.2516005 · Cited by 77
Artificial intelligence will soon change the landscape of medical physics research and practice
Xing, L., Krupinski, E. A., & Cai, J. (2018) · Medical Physics · doi:10.1002/mp.12831 · Cited by 71
Whole Brain Segmentation and Labeling from CT Using Synthetic MR Images
Zhao, C., Carass, A., Lee, J., He, Y., & Prince, J. L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67389-9_34 · Cited by 70
Multi-task neural networks for joint hippocampus segmentation and clinical score regression
Cao, L., Li, L., Zheng, J., Fan, X., Yin, F., Shen, H., & Zhang, J. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5581-1 · Cited by 70
Evaluation of a commercial automatic treatment planning system for prostate cancers
Nawa, K., Haga, A., Nomoto, A. K., Sarmiento, R. A., Shiraishi, K., Yamashita, H., & Nakagawa, K. (2017) · Medical dosimetry · doi:10.1016/j.meddos.2017.03.004 · Cited by 69
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
Hou, B., Alansary, A., McDonagh, S., Davidson, A., Rutherford, M., Hajnal, J. V., Rueckert, D., Glocker, B., & Kainz, B. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66185-8_34 · Cited by 68
Sample size requirements for knowledge‐based treatment planning
Boutilier, J. J., Craig, T., Sharpe, M. B., & Chan, T. C. Y. (2016) · Medical Physics · doi:10.1118/1.4941363 · Cited by 63
Spotting L3 slice in CT scans using deep convolutional network and transfer learning
Belharbi, S., Chatelain, C., Hérault, R., Adam, S., Thureau, S., Chastan, M., & Modzelewski, R. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.05.018 · Cited by 63
Application of unsupervised analysis techniques to lung cancer patient data
Lynch, C. M., van Berkel, V. H., & Frieboes, H. B. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184370 · Cited by 63
Geometrical and dosimetrical uncertainties in hypofractionated radiotherapy of the lung: A review
Schwarz, M., Cattaneo, G. M., & Marrazzo, L. (2017) · Physica Medica · doi:10.1016/j.ejmp.2017.02.011 · Cited by 59
MRI ‐based prostate cancer detection with high‐level representation and hierarchical classification
Zhu, Y., Wang, L., Liu, M., Qian, C., Yousuf, A. N., Oto, A., & Shen, D. G. (2017) · Medical Physics · doi:10.1002/mp.12116 · Cited by 58
Clinical Relation Extraction with Deep Learning
Lv, X., Guan, Y., Yang, J., & Wu, J. (2016) · International Journal of Hybrid Information Technology · doi:10.14257/ijhit.2016.9.7.22 · Cited by 58
Automatic planning on hippocampal avoidance whole-brain radiotherapy
Wang, S., Zheng, D., Zhang, C., Ma, R., Bennion, N. R., Lei, Y., Zhu, X., Enke, C. A., & Zhou, S. (2017) · Medical dosimetry · doi:10.1016/j.meddos.2016.12.002 · Cited by 56
Demystifying Big Data and Machine Learning for Healthcare
Natarajan, P., Frenzel, J. C., & Smaltz, D. H. (2017) · CRC Press eBooks · doi:10.1201/9781315389325 · Cited by 54
A review of image-guided radiotherapy
Chen, G. T. Y., Sharp, G. C., & Mori, S. (2008) · Radiological Physics and Technology · doi:10.1007/s12194-008-0045-y · Cited by 53
Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion
Ma, L., Guo, R., Zhang, G., Tade, F. I., Schuster, D. M., Nieh, P. T., Master, V. A., & Fei, B. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2255755 · Cited by 53
Metal artefact reduction for accurate tumour delineation in radiotherapy
Kovacs, D. G., Rechner, L. A., Appelt, A. L., Berthelsen, A. K., Costa, J. C., Friborg, J., Persson, G. F., Bangsgaard, J. P., Specht, L., & Aznar, M. C. (2017) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2017.09.029 · Cited by 53
Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study
Sunwoo, L., Kim, Y. J., Choi, S. H., Kim, K.-G., Kang, J. H., Kang, Y., Bae, Y. J., Yoo, R.-E., Kim, J., Lee, K. J., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0178265 · Cited by 52
Das, S. K., Zhou, S., Zhang, J., Yin, F.-F., Dewhirst, M. W., & Marks, L. B. (2007) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2007.03.064 · Cited by 51
Trullo, R., Petitjean, C., Nie, D., Shen, D. G., & Ruan, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9_3 · Cited by 51
Automatic Tumor Segmentation with Deep Convolutional Neural Networks for Radiotherapy Applications
Wang, Y., Zu, C., Hu, G., Luo, Y., Ma, Z., He, K., Wu, X., & Zhou, J. (2018) · Neural Processing Letters · doi:10.1007/s11063-017-9759-3 · Cited by 51
Learning-Based Multi-Label Segmentation of Transrectal Ultrasound Images for Prostate Brachytherapy
Nouranian, S., Ramezani, M., Spadinger, I., Morris, W. J., Salcudean, S. E., & Abolmaesumi, P. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2502540 · Cited by 50
Dolz, J., Betrouni, N., Quidet, M., Kharroubi, D., Leroy, H. A., Reyns, N., Massoptier, L., & Vermandel, M. (2016) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2016.03.003 · Cited by 48
Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology
Salazar, B. M., Balczewski, E. A., Ung, C. Y., & Zhu, S. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms18010037 · Cited by 47
Deep Learning for Image Retrieval: What Works and What Doesn't
Wang, H., Cai, Y., Zhang, Y., Pan, H., Lv, W., & Han, H. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.121 · Cited by 45
DeepInfer: open-source deep learning deployment toolkit for image-guided therapy
Mehrtash, A., Pesteie, M., Hetherington, J., Behringer, P. A., Kapur, T., Wells, W. M., Rohling, R. N., Fedorov, A., & Abolmaesumi, P. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2256011 · Cited by 45
Nguyen, D. T., O'Brien, R. T., Kim, J., Huang, C., Wilton, L., Greer, P. B., Legge, K., Booth, J., Poulsen, P. R., Martin, J. M., et al. (2017) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2017.02.013 · Cited by 45
Nguyen, D. T. D., Long, T., Jia, X., Lu, W., Gu, X., Iqbal, Z., & Jiang, S. B. (2017) · arXiv (Cornell University) · Cited by 42
Chen, Q., Xu, X., Hu, S., Xiao, L., Zou, Q., & Li, Y. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2279021 · Cited by 41
Fan, J., Wang, J., Zhang, Z., & Hu, W. (2017) · Medical Physics · doi:10.1002/mp.12232 · Cited by 40
Wang, H., Dong, P., Liu, H., & Xing, L. (2016) · Medical Physics · doi:10.1002/mp.12058 · Cited by 38
Big Data Analytics for Prostate Radiotherapy
Coates, J. T. T., Souhami, L., & Naqa, I. E. (2016) · Frontiers in Oncology · doi:10.3389/fonc.2016.00149 · Cited by 36
Improving the efficiency of breast radiotherapy treatment planning using a semi‐automated approach
Mitchell, A., Wai, P., Colgan, R. M., Kirby, A. M., & Donovan, E. M. (2016) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12006 · Cited by 35
Benedict, S., Hoffman, K. E., Martel, M. K., Abernethy, A. P., Asher, A. L., Capala, J., Chen, R. C., Chera, B. S., Couch, J., Deye, J. A., et al. (2016) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2016.03.006 · Cited by 33
Terunuma, T., Tokui, A., & Sakae, T. (2017) · Radiological Physics and Technology · doi:10.1007/s12194-017-0435-0 · Cited by 30
Unsupervised Deep Learning for Hippocampus Segmentation in 7.0 Tesla MR Images
Kim, M., Guorong Wu, A., & Shen, D. G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02267-3_1 · Cited by 28
Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging
Shahedi, M., Cool, D. W., Bauman, G. S., Bastian‐Jordan, M., Fenster, A., & Ward, A. D. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-9964-7 · Cited by 26
Accurate model-based segmentation of gynecologic brachytherapy catheter collections in MRI-images
Mastmeyer, A., Pernelle, G., Ma, R., Barber, L. A., & Kapur, T. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.06.011 · Cited by 26
The Potential Role of Magnetic Resonance Spectroscopy in Image-Guided Radiotherapy
Nguyen, M. L. H., Willows, B., Khan, R., Chi, A., Kim, L., Nour, S. G., Sroka, T. C., Kerr, C., Godinez, J., Mills, M. B., et al. (2014) · Frontiers in Oncology · doi:10.3389/fonc.2014.00091 · Cited by 25
Naqa, I. E., Brock, K., Yu, Y., Langen, K., & Klein, E. (2017) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2017.06.011 · Cited by 23
Deep-Learning Based Prediction of Achievable Dose for Personalizing Inverse Treatment Planning
Mardani, M., Dong, P., & Xing, L. (2016) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2016.06.1685 · Cited by 22
Research works printed but not linked: 6
- Bengio, 2009, Learning deep architectures for AI, found. Trends® mach, Learning, 2, 1
- de Brebisson, 2015, Deep neural networks for anatomical brain segmentation, in: proc. IEEE Conf. Comput. Vis. Pattern Recognit, Works, 20
- Whitey, 2008, A review of medial image segmentation, Int. J. Bioelectromagn, 10, 125
- MICCAI BRATS - The Multimodal Brain Tumor Segmentation Challenge. https://www.med.upenn.edu/sbia/brats2018.html.
- Yuan, 2017, Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance, IEEE Trans. Med. Imaging. PP, 10.1109/TMI.2017.2695227 · DOI · Paper page
- Belharbi, 2017, Spotting L3 slice in CT scans using deep convolutional network and transfer learning, Comput. Biol. Med., 10.1016/j.compbiomed.2017.05.018 · DOI
Cited by
Sorted by citations
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012
Medical image segmentation using deep learning: A survey
Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022) · IET Image Processing · doi:10.1049/ipr2.12419 · Cited by 1,012
A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaševičius, R., & de Albuquerque, V. H. C. (2020) · Applied Sciences · doi:10.3390/app10020559 · Cited by 753
Deep learning in medical image registration: a review
Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ab843e · Cited by 661
Metadata
- Authors
- Meyer, P., Noblet, V., Mazzara, C., & Lallement, A.
- Affiliations
- Institut Strauss (FR)
- Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie (FR)
- Corresponding author
- Meyer, Philippe
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 98
- Pages
- 126-146
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W2804608955
- Cited by
- 302
- References
- 237
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 38 · 2023: 38 · 2022: 46 · 2021: 60 · 2020: 48 · 2019: 37 · 2018: 11
- FWCI
- 5.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- deep learning, radiotherapy, radiotherapy treatment planning, convolutional neural networks, artificial intelligence in healthcare, machine learning, network architecture, patient workflow
- MeSH terms
- Deep Learning, Humans, Nasopharyngeal Neoplasms, Radiotherapy, Radiotherapy Planning, Computer-Assisted
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review