NiftyNet: a deep-learning platform for medical imaging

•An open-source platform is implemented based on TensorFlow APIs for deep learning in medical imaging domain.•A modular implementation of the typical medical imaging machine learning pipeline facilitates (1) warm starts with established pre-trained networks, (2) adapting existing neural network arch...

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Bibliographic Details
Published in:	Computer methods and programs in biomedicine Vol. 158; pp. 113 - 122
Main Authors:	Gibson, Eli, Li, Wenqi, Sudre, Carole, Fidon, Lucas, Shakir, Dzhoshkun I., Wang, Guotai, Eaton-Rosen, Zach, Gray, Robert, Doel, Tom, Hu, Yipeng, Whyntie, Tom, Nachev, Parashkev, Modat, Marc, Barratt, Dean C., Ourselin, Sébastien, Cardoso, M. Jorge, Vercauteren, Tom
Format:	Journal Article
Language:	English
Published:	Ireland Elsevier B.V 01.05.2018 Elsevier Scientific Publishers
Subjects:	Abdomen - diagnostic imaging Brain - diagnostic imaging Computer Simulation Convolutional neural network Databases, Factual Deep learning Diagnostic Imaging - instrumentation Diagnostic Imaging - methods Generative adversarial network Humans Image Processing, Computer-Assisted - instrumentation Image Processing, Computer-Assisted - methods Image regression Machine Learning Magnetic Resonance Imaging Medical image analysis Neural Networks, Computer Segmentation Ultrasonography Deep learning Segmentation Convolutional neural network Medical image analysis Generative adversarial network Image regression
ISSN:	0169-2607, 1872-7565, 1872-7565
Online Access:	Get full text
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Abstract	•An open-source platform is implemented based on TensorFlow APIs for deep learning in medical imaging domain.•A modular implementation of the typical medical imaging machine learning pipeline facilitates (1) warm starts with established pre-trained networks, (2) adapting existing neural network architectures to new problems, and (3) rapid prototyping of new solutions.•Three deep-learning applications, including segmentation, regression, image generation and representation learning, are presented as concrete examples illustrating the platform’s key features. Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default. We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.
AbstractList	• An open-source platform is implemented based on TensorFlow APIs for deep learning in medical imaging domain. • A modular implementation of the typical medical imaging machine learning pipeline facilitates (1) warm starts with established pre-trained networks, (2) adapting existing neural network architectures to new problems, and (3) rapid prototyping of new solutions. • Three deep-learning applications, including segmentation, regression, image generation and representation learning, are presented as concrete examples illustrating the platform’s key features. •An open-source platform is implemented based on TensorFlow APIs for deep learning in medical imaging domain.•A modular implementation of the typical medical imaging machine learning pipeline facilitates (1) warm starts with established pre-trained networks, (2) adapting existing neural network architectures to new problems, and (3) rapid prototyping of new solutions.•Three deep-learning applications, including segmentation, regression, image generation and representation learning, are presented as concrete examples illustrating the platform’s key features. Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default. We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications. Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon.BACKGROUND AND OBJECTIVESMedical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon.The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default.METHODSThe NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default.We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses.RESULTSWe present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses.The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.CONCLUSIONSThe NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications. Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default. We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.
Author	Eaton-Rosen, Zach Doel, Tom Barratt, Dean C. Vercauteren, Tom Gray, Robert Whyntie, Tom Ourselin, Sébastien Fidon, Lucas Sudre, Carole Modat, Marc Li, Wenqi Wang, Guotai Gibson, Eli Shakir, Dzhoshkun I. Hu, Yipeng Cardoso, M. Jorge Nachev, Parashkev
AuthorAffiliation	c Institute of Neurology, University College London, UK b Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK d National Hospital for Neurology and Neurosurgery, London, UK a Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK
AuthorAffiliation_xml	– name: a Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK – name: d National Hospital for Neurology and Neurosurgery, London, UK – name: b Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK – name: c Institute of Neurology, University College London, UK
Author_xml	– sequence: 1 givenname: Eli orcidid: 0000-0001-9207-7280 surname: Gibson fullname: Gibson, Eli organization: Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK – sequence: 2 givenname: Wenqi orcidid: 0000-0002-7432-7386 surname: Li fullname: Li, Wenqi email: wenqi.li@ucl.ac.uk organization: Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK – sequence: 3 givenname: Carole orcidid: 0000-0001-5753-428X surname: Sudre fullname: Sudre, Carole organization: Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK – sequence: 4 givenname: Lucas orcidid: 0000-0003-1450-1634 surname: Fidon fullname: Fidon, Lucas organization: Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK – sequence: 5 givenname: Dzhoshkun I. orcidid: 0000-0003-3009-4178 surname: Shakir fullname: Shakir, Dzhoshkun I. organization: Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK – sequence: 6 givenname: Guotai orcidid: 0000-0002-8632-158X surname: Wang fullname: Wang, Guotai organization: Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK – sequence: 7 givenname: Zach surname: Eaton-Rosen fullname: Eaton-Rosen, Zach organization: Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK – sequence: 8 givenname: Robert surname: Gray fullname: Gray, Robert organization: Institute of Neurology, University College London, UK – sequence: 9 givenname: Tom orcidid: 0000-0001-8092-9378 surname: Doel fullname: Doel, Tom organization: Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK – sequence: 10 givenname: Yipeng orcidid: 0000-0003-4902-0486 surname: Hu fullname: Hu, Yipeng organization: Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK – sequence: 11 givenname: Tom orcidid: 0000-0003-0501-1377 surname: Whyntie fullname: Whyntie, Tom organization: Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK – sequence: 12 givenname: Parashkev surname: Nachev fullname: Nachev, Parashkev organization: Institute of Neurology, University College London, UK – sequence: 13 givenname: Marc orcidid: 0000-0002-5277-8530 surname: Modat fullname: Modat, Marc organization: Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK – sequence: 14 givenname: Dean C. surname: Barratt fullname: Barratt, Dean C. organization: Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK – sequence: 15 givenname: Sébastien surname: Ourselin fullname: Ourselin, Sébastien organization: Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK – sequence: 16 givenname: M. Jorge surname: Cardoso fullname: Cardoso, M. Jorge organization: Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK – sequence: 17 givenname: Tom orcidid: 0000-0003-1794-0456 surname: Vercauteren fullname: Vercauteren, Tom organization: Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29544777$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1016/j.cmpb.2016.11.004 10.1016/j.neuroimage.2004.07.051 10.1016/j.media.2016.10.004 10.1109/TMI.2009.2035616 10.1109/TMI.2014.2377694 10.3389/fninf.2013.00045 10.1146/annurev-bioeng-071516-044442 10.1109/42.476112 10.1007/s11548-014-1124-7 10.1016/j.neuroimage.2010.09.025 10.1109/TMI.2014.2340135 10.1016/j.cmpb.2009.09.002 10.1007/s11548-014-1118-5 10.1016/j.media.2017.07.005 10.1006/nimg.1998.0396 10.1007/s11548-013-0840-8 10.1006/nimg.1998.0395
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Keywords	Deep learning Segmentation Convolutional neural network Medical image analysis Generative adversarial network Image regression
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. Wenqi Li and Eli Gibson contributed equally to this work.
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References	B. Landman, Z. Xu, J.E. Igelsias, M. Styner, T.R. Langerak, A. Klein, Multi-atlas labeling beyond the cranial vault, 2015, URL Çiçek, Abdulkadir, Lienkamp, Brox, Ronneberger (bib0011) 2016 Milletari, Navab, Ahmadi (bib0040) 2016 Roth, Lu, Seff, Cherry, Hoffman, Wang, Liu, Turkbey, Summers (bib0048) 2014 Abadi, Agarwal, Barham, Brevdo, Chen, Citro, Corrado, Davis, Dean, Devin, Ghemawat, Goodfellow, Harp, Irving, Isard, Jia, Jozefowicz, Kaiser, Kudlur, Levenberg, Mane, Monga, Moore, Murray, Olah, Schuster, Shlens, Steiner, Sutskever, Talwar, Tucker, Vanhoucke, Vasudevan, Viegas, Vinyals, Warden, Wattenberg, Wicke, Yu, Zheng (bib0001) 2016 D. Mané, et al., TensorBoard: TensorFlow’s visualization toolkit, 2015 L. Berger, E. Hyde, M.J. Cardoso, S. Ourselin, An adaptive sampling scheme to efficiently train fully convolutional networks for semantic segmentation, (2017). ArXiv e-prints arXiv Shen, Wu, Suk (bib0051) 2017 Roth, Farag, Turkbey, Lu, Liu, Summers (bib0047) 1994 S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, E. Shelhamer, cuDNN: efficient primitives for deep learning, arXiv Doel, Shakir, Pratt, Aertsen, Moggridge, Bellon, David, Deprest, Vercauteren, Ourselin (bib0016) 2017; 139 Li, Wang, Fidon, Ourselin, Cardoso, Vercauteren (bib0033) 2017 Smith, Jenkinson, Woolrich, Beckmann, Behrens, Johansen-Berg, Bannister, De Luca, Drobnjak, Flitney, Niazy, Saunders, Vickers, Zhang, De Stefano, Brady, Matthews (bib0052) 2004; 23 J.L. Ba, J.R. Kiros, G.E. Hinton, Layer normalization (2016). arXiv M. Reynolds, et al., Sonnet, 2017 Dale, Fischl, Sereno (bib0014) 1999; 9 . Johnsen, Taylor, Clarkson, Hipwell, Modat, Eiben, Han, Hu, Mertzanidou, Hawkes, Ourselin (bib0029) 2015; 10 Burgos, Cardoso, Thielemans, Modat, Pedemonte, Dickson, Barnes, Ahmed, Mahoney, Schott, Duncan, Atkinson, Arridge, Hutton, Ourselin (bib0007) 2014; 33 Klein, Staring, Murphy, Viergever, Pluim (bib0031) 2010; 29 Gamma, Vlissides, Johnson, Helm (bib0020) 1994 Cardoso, Clarkson, Modat, Ourselin (bib0008) 2012 G. Wang, W. Li, S. Ourselin, T. Vercauteren, Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks, Proc. Multimodal Brain Tumor Segmentation (BRATS) Challenge 2017 - MICCAI workshop. 2016 (2017). arXiv Garcia-Peraza-Herrera, Li, Fidon, Gruijthuijsen, Devreker, Attilakos, Deprest, Poorten, Stoyanov, Vercauteren, Ourselin (bib0021) 2017 Gibson, Robu, Thompson, Edwards, Schneider, Gurusamy, Davidson, Hawkes, Barratt, Clarkson (bib0024) 2017; vol. 10135 G. Wang, M.A. Zuluaga, W. Li, R. Pratt, P.A. Patel, M. Aertsen, T. Doel, A.L. David, J. Deprest, S. Ourselin, T. Vercauteren, DeepIGeoS: a deep interactive geodesic framework for medical image segmentation, (2017). arXiv I. Goodfellow, NIPS 2016 tutorial: generative adversarial networks (2016). arXiv Seide, Agarwal (bib0050) 2016 Fischl, Sereno, Dale (bib0019) 1999; 9 Menze, Jakab, Bauer, Kalpathy-Cramer, Farahani, Kirby, Burren, Porz, Slotboom, Wiest, Lanczi, Gerstner, Weber, Arbel, Avants, Ayache, Buendia, Collins, Cordier, Corso, Criminisi, Das, Delingette, Ç. Demiralp, Durst, Dojat, Doyle, Festa, Forbes, Geremia, Glocker, Golland, Guo, Hamamci, Iftekharuddin, Jena, John, Konukoglu, Lashkari, Mariz, Meier, Pereira, Precup, Price, Raviv, Reza, Ryan, Sarikaya, Schwartz, Shin, Shotton, Silva, Sousa, Subbanna, Szekely, Taylor, Thomas, Tustison, Unal, Vasseur, Wintermark, Ye, Zhao, Zhao, Zikic, Prastawa, Reyes, Leemput (bib0039) 2015; 34 Modat, Ridgway, Taylor, Lehmann, Barnes, Hawkes, Fox, Ourselin (bib0042) 2010; 98 Vedaldi, Lenc (bib0054) 2015 F. Chollet, et al., Keras, 2015 Ioffe (bib0027) 2017 Hu, Gibson, Lee, Xie, Barratt, Vercauteren, Noble (bib0026) 2017 M. Brett, M. Hanke, B. Cipollini, M.-A. Côté, C. Markiewicz, S. Gerhard, E. Larson, Nibabel, 2016, Online. doi Collobert, Kavukcuoglu, Farabet (bib0013) 2011 Gibson, Giganti, Hu, Bonmati, Bandula, Gurusamy, Davidson, Pereira, Clarkson, Barratt (bib0022) 2017 S. Dieleman, J. Schlter, C. Raffel, E. Olson, S.K. Snderby, D. Nouri, D. Maturana, M. Thoma, E. Battenberg, J. Kelly, J.D. Fauw, M. Heilman, D.M. de Almeida, B. McFee, H. Weideman, G. TakÃ!‘cs, P. de Rivaz, J. Crall, G. Sanders, K. Rasul, C. Liu, G. French, J. Degrave, Lasagne: first release, 2015, 10.5281/zenodo.27878 T. Preston-Werner, Semantic versioning, Technical Report, T. Preston-Werner, 2015. URL Bastien, Lamblin, Pascanu, Bergstra, Goodfellow, Bergeron, Bouchard, Bengio (bib0004) 2012 Lo, Lou, Lin, Freedman, Chien, Mun (bib0035) 1995; 14 L. Fidon, W. Li, L.C. Garcia-Peraza-Herrera, J. Ekanayake, N. Kitchen, S. Ourselin, T. Vercauteren, Generalised Wasserstein Dice score for imbalanced multi-class segmentation using holistic convolutional networks, (2017). arXiv Gibson, Giganti, Hu, Bonmati, Bandula, Gurusamy, Davidson, Pereira, Clarkson, Barratt (bib0023) 2017 Litjens, Kooi, Bejnordi, Setio, Ciompi, Ghafoorian, van der Laak, van Ginneken, Sánchez (bib0034) 2017; 42 accessed July 2017. 10.7303/syn3193805. Pieper, Lorensen, Schroeder, Kikinis (bib0044) 2006 Kamnitsas, Ledig, Newcombe, Simpson, Kane, Menon, Rueckert, Glocker (bib0030) 2017; 36 M. Mirza, S. Osindero, Conditional generative adversarial nets. Proc. NIPS 2016 Workshop on Adversarial Training. 2016 (2014), arXiv Nolden, Zelzer, Seitel, Wald, Müller, Franz, Maleike, Fangerau, Baumhauer, Maier-Hein, Maier-Hein, Meinzer, Wolf (bib0043) 2013; 8 Lowekamp, Chen, Ibáñez, Blezek (bib0036) 2013; 7 Sudre, Li, Vercauteren, Ourselin, Cardoso (bib0053) 2017 Dong, Supratak, Mai, Liu, Oehmichen, Yu, Guo (bib0017) 2017 Zhou, Ito, Takayama, Wang, Hara, Fujita (bib0057) 2016 Avants, Tustison, Song, Cook, Klein, Gee (bib0002) 2011; 54 Jia, Shelhamer, Donahue, Karayev, Long, Girshick, Guadarrama, Darrell (bib0028) 2014 Clarkson, Zombori, Thompson, Totz, Song, Espak, Johnsen, Hawkes, Ourselin (bib0012) 2015; 10 Mehrtash, Pesteie, Hetherington, Behringer, Kapur, Wells III, Rohling, Fedorov, Abolmaesumi (bib0038) 2017; 10135 T. Salimans, D.P. Kingma, Weight normalization: a simple reparameterization to accelerate training of deep neural networks (2016). arXiv 10.1016/j.cmpb.2018.01.025_bib0025 Litjens (10.1016/j.cmpb.2018.01.025_bib0034) 2017; 42 Collobert (10.1016/j.cmpb.2018.01.025_bib0013) 2011 Ioffe (10.1016/j.cmpb.2018.01.025_bib0027) 2017 Lowekamp (10.1016/j.cmpb.2018.01.025_bib0036) 2013; 7 Clarkson (10.1016/j.cmpb.2018.01.025_bib0012) 2015; 10 Dale (10.1016/j.cmpb.2018.01.025_bib0014) 1999; 9 Hu (10.1016/j.cmpb.2018.01.025_bib0026) 2017 10.1016/j.cmpb.2018.01.025_bib0018 Shen (10.1016/j.cmpb.2018.01.025_bib0051) 2017 Cardoso (10.1016/j.cmpb.2018.01.025_bib0008) 2012 Gamma (10.1016/j.cmpb.2018.01.025_bib0020) 1994 10.1016/j.cmpb.2018.01.025_bib0015 Seide (10.1016/j.cmpb.2018.01.025_bib0050) 2016 10.1016/j.cmpb.2018.01.025_bib0010 Mehrtash (10.1016/j.cmpb.2018.01.025_bib0038) 2017; 10135 Burgos (10.1016/j.cmpb.2018.01.025_bib0007) 2014; 33 10.1016/j.cmpb.2018.01.025_bib0055 Vedaldi (10.1016/j.cmpb.2018.01.025_bib0054) 2015 10.1016/j.cmpb.2018.01.025_bib0056 Bastien (10.1016/j.cmpb.2018.01.025_bib0004) 2012 Abadi (10.1016/j.cmpb.2018.01.025_sbref0001) 2016 Doel (10.1016/j.cmpb.2018.01.025_bib0016) 2017; 139 Kamnitsas (10.1016/j.cmpb.2018.01.025_bib0030) 2017; 36 Fischl (10.1016/j.cmpb.2018.01.025_bib0019) 1999; 9 Li (10.1016/j.cmpb.2018.01.025_bib0033) 2017 10.1016/j.cmpb.2018.01.025_bib0009 Johnsen (10.1016/j.cmpb.2018.01.025_bib0029) 2015; 10 Menze (10.1016/j.cmpb.2018.01.025_bib0039) 2015; 34 10.1016/j.cmpb.2018.01.025_bib0003 10.1016/j.cmpb.2018.01.025_bib0005 Roth (10.1016/j.cmpb.2018.01.025_bib0047) 1994 10.1016/j.cmpb.2018.01.025_bib0049 10.1016/j.cmpb.2018.01.025_bib0006 Avants (10.1016/j.cmpb.2018.01.025_bib0002) 2011; 54 10.1016/j.cmpb.2018.01.025_bib0045 10.1016/j.cmpb.2018.01.025_bib0046 10.1016/j.cmpb.2018.01.025_bib0041 Zhou (10.1016/j.cmpb.2018.01.025_bib0057) 2016 Milletari (10.1016/j.cmpb.2018.01.025_bib0040) 2016 Sudre (10.1016/j.cmpb.2018.01.025_bib0053) 2017 Modat (10.1016/j.cmpb.2018.01.025_bib0042) 2010; 98 Lo (10.1016/j.cmpb.2018.01.025_bib0035) 1995; 14 Gibson (10.1016/j.cmpb.2018.01.025_sbref0015) 2017 Nolden (10.1016/j.cmpb.2018.01.025_bib0043) 2013; 8 Pieper (10.1016/j.cmpb.2018.01.025_bib0044) 2006 Roth (10.1016/j.cmpb.2018.01.025_bib0048) 2014 10.1016/j.cmpb.2018.01.025_bib0037 10.1016/j.cmpb.2018.01.025_bib0032 Smith (10.1016/j.cmpb.2018.01.025_bib0052) 2004; 23 Garcia-Peraza-Herrera (10.1016/j.cmpb.2018.01.025_bib0021) 2017 Gibson (10.1016/j.cmpb.2018.01.025_bib0023) 2017 Dong (10.1016/j.cmpb.2018.01.025_bib0017) 2017 Klein (10.1016/j.cmpb.2018.01.025_bib0031) 2010; 29 Jia (10.1016/j.cmpb.2018.01.025_bib0028) 2014 Gibson (10.1016/j.cmpb.2018.01.025_sbref0017) 2017; vol. 10135 Çiçek (10.1016/j.cmpb.2018.01.025_bib0011) 2016
References_xml	– reference: G. Wang, W. Li, S. Ourselin, T. Vercauteren, Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks, Proc. Multimodal Brain Tumor Segmentation (BRATS) Challenge 2017 - MICCAI workshop. 2016 (2017). arXiv: – start-page: 2135 year: 2016 ident: bib0050 article-title: CNTK: Microsoft’s open-source deep-learning toolkit publication-title: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – reference: M. Mirza, S. Osindero, Conditional generative adversarial nets. Proc. NIPS 2016 Workshop on Adversarial Training. 2016 (2014), arXiv: – year: 2014 ident: bib0048 article-title: A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations publication-title: Proceedings of the MICCAI – volume: 54 start-page: 2033 year: 2011 end-page: 2044 ident: bib0002 article-title: A reproducible evaluation of ANTs similarity metric performance in brain image registration publication-title: Neuroimage – reference: M. Reynolds, et al., Sonnet, 2017, – start-page: 675 year: 2014 end-page: 678 ident: bib0028 article-title: Caffe: Convolutional architecture for fast feature embedding publication-title: Proceedings of the 22nd ACM International Conference on Multimedia (ACMMM) – reference: T. Preston-Werner, Semantic versioning, Technical Report, T. Preston-Werner, 2015. URL: – reference: F. Chollet, et al., Keras, 2015, – volume: 10 start-page: 301 year: 2015 end-page: 316 ident: bib0012 article-title: The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging publication-title: Int. J. Comput. Assist. Radiol. Surg. – start-page: 565 year: 2016 end-page: 571 ident: bib0040 article-title: V-Net: Fully convolutional neural networks for volumetric medical image segmentation publication-title: Proceedings of the Fourth International Conference on 3D Vision (3DV) – volume: 10135 year: 2017 ident: bib0038 article-title: DeepInfer: Open-source deep learning deployment toolkit for image-guided therapy publication-title: Proceedings of the SPIE, Medical Imaging – volume: 7 year: 2013 ident: bib0036 article-title: The design of SimpleITK publication-title: Front. Neuroinf. – volume: 10 start-page: 1077 year: 2015 end-page: 1095 ident: bib0029 article-title: NiftySim: a GPU-based nonlinear finite element package for simulation of soft tissue biomechanics publication-title: Int. J. Comput. Assist. Radiol. Surg. – volume: 34 start-page: 1993 year: 2015 end-page: 2024 ident: bib0039 article-title: The multimodal brain tumor image segmentation benchmark (BraTS) publication-title: IEEE Trans. Med. Imaging – volume: 9 start-page: 195 year: 1999 end-page: 207 ident: bib0019 article-title: Cortical surface-based analysis: II: inflation, flattening, and a surface-based coordinate system publication-title: Neuroimage – reference: J.L. Ba, J.R. Kiros, G.E. Hinton, Layer normalization (2016). arXiv: – start-page: 348 year: 2017 end-page: 360 ident: bib0033 article-title: On the compactness, efficiency, and representation of 3D convolutional networks: Brain parcellation as a pretext task publication-title: Proceedings of Information Processing in Medical Imaging (IPMI) – reference: M. Brett, M. Hanke, B. Cipollini, M.-A. Côté, C. Markiewicz, S. Gerhard, E. Larson, Nibabel, 2016, Online. doi: – year: 2017 ident: bib0022 article-title: Automatic multi-organ segmentation on abdominal CT with dense v-networks publication-title: IEEE Trans. Med. Imaging – year: 2015 ident: bib0054 article-title: MatConvNet – convolutional neural networks for MATLAB publication-title: Proceedings of the ACMM – start-page: 424 year: 2016 end-page: 432 ident: bib0011 article-title: 3D U-net: learning dense volumetric segmentation from sparse annotation publication-title: Proceedings of the MICCAI – volume: 29 start-page: 196 year: 2010 end-page: 205 ident: bib0031 article-title: Elastix: a toolbox for intensity-based medical image registration publication-title: IEEE Trans. Med. Imaging – reference: L. Berger, E. Hyde, M.J. Cardoso, S. Ourselin, An adaptive sampling scheme to efficiently train fully convolutional networks for semantic segmentation, (2017). ArXiv e-prints arXiv: – reference: S. Dieleman, J. Schlter, C. Raffel, E. Olson, S.K. Snderby, D. Nouri, D. Maturana, M. Thoma, E. Battenberg, J. Kelly, J.D. Fauw, M. Heilman, D.M. de Almeida, B. McFee, H. Weideman, G. TakÃ!‘cs, P. de Rivaz, J. Crall, G. Sanders, K. Rasul, C. Liu, G. French, J. Degrave, Lasagne: first release, 2015, 10.5281/zenodo.27878 – volume: 14 start-page: 711 year: 1995 end-page: 718 ident: bib0035 article-title: Artificial convolution neural network techniques and applications for lung nodule detection publication-title: IEEE Trans. Med. Imaging – year: 2017 ident: bib0017 article-title: TensorLayer: a versatile library for efficient deep learning development – start-page: 698 year: 2006 end-page: 701 ident: bib0044 article-title: The NA-MIC kit: ITK, VTK, pipelines, grids and 3D Slicer as an open platform for the medical image computing community publication-title: Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) – reference: I. Goodfellow, NIPS 2016 tutorial: generative adversarial networks (2016). arXiv: – volume: 33 start-page: 2332 year: 2014 end-page: 2341 ident: bib0007 article-title: Attenuation correction synthesis for hybrid PET-MR scanners: Application to brain studies publication-title: Med. Imaging IEEE Trans. – start-page: 5717 year: 2017 end-page: 5722 ident: bib0021 article-title: ToolNet: holistically-nested real-time segmentation of robotic surgical tools publication-title: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC – year: 2012 ident: bib0008 article-title: NiftySeg: open-source software for medical image segmentation, label fusion and cortical thickness estimation publication-title: Proceedings of the ISBI Workshop on Open Source Medical Image Analysis Software – year: 2011 ident: bib0013 article-title: Torch7: A MATLAB-like environment for machine learning publication-title: Proceedings of the NIPS Workshop on Algorithms, Systems, and Tools for Learning at Scale (Big Learning) – reference: , accessed July 2017. 10.7303/syn3193805. – reference: D. Mané, et al., TensorBoard: TensorFlow’s visualization toolkit, 2015, – year: 2017 ident: bib0023 article-title: Towards image-guided pancreas and biliary endoscopy: automatic multi-organ segmentation on abdominal CT with dense dilated networks publication-title: Proceedings of the 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) – volume: 9 start-page: 179 year: 1999 end-page: 194 ident: bib0014 article-title: Cortical surface-based analysis:I. Segmentation and surface reconstruction publication-title: Neuroimage – reference: L. Fidon, W. Li, L.C. Garcia-Peraza-Herrera, J. Ekanayake, N. Kitchen, S. Ourselin, T. Vercauteren, Generalised Wasserstein Dice score for imbalanced multi-class segmentation using holistic convolutional networks, (2017). arXiv: – volume: 98 start-page: 278 year: 2010 end-page: 284 ident: bib0042 article-title: Fast free-form deformation using graphics processing units publication-title: Comput. Methods Progr. Biomed. – reference: S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, E. Shelhamer, cuDNN: efficient primitives for deep learning, arXiv: – start-page: 111 year: 2016 end-page: 120 ident: bib0057 article-title: Three-dimensional CT image segmentation by combining 2D fully convolutional network with 3D majority voting publication-title: Proceedings of the LABELS – reference: B. Landman, Z. Xu, J.E. Igelsias, M. Styner, T.R. Langerak, A. Klein, Multi-atlas labeling beyond the cranial vault, 2015, URL: – year: 2012 ident: bib0004 article-title: Theano: new features and speed improvements publication-title: Proceedings of the Workshop on Deep Learning and Unsupervised Feature Learning NIPS – reference: G. Wang, M.A. Zuluaga, W. Li, R. Pratt, P.A. Patel, M. Aertsen, T. Doel, A.L. David, J. Deprest, S. Ourselin, T. Vercauteren, DeepIGeoS: a deep interactive geodesic framework for medical image segmentation, (2017). arXiv: – start-page: 221 year: 2017 end-page: 248 ident: bib0051 article-title: Deep learning in medical image analysis publication-title: Ann. Rev. Biomed. Eng. – start-page: 1942 year: 2017 end-page: 1950 ident: bib0027 article-title: Batch renormalization: towards reducing minibatch dependence in batch-normalized models publication-title: Advances in Neural Information Processing Systems 30 – volume: 36 start-page: 61 year: 2017 end-page: 78 ident: bib0030 article-title: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation publication-title: Med. Image Anal. – reference: T. Salimans, D.P. Kingma, Weight normalization: a simple reparameterization to accelerate training of deep neural networks (2016). arXiv: – year: 2017 ident: bib0026 article-title: Freehand ultrasound image simulation with spatially-conditioned generative adversarial networks publication-title: Proceedings of MICCAI Workshop on Reconstruction and Analysis of Moving Body Organs (RAMBO) – volume: 8 start-page: 607 year: 2013 end-page: 620 ident: bib0043 article-title: The medical imaging interaction toolkit: challenges and advances publication-title: Int. J. Comput. Assist. Radiol. Surg. – volume: 23 start-page: S208 year: 2004 end-page: S219 ident: bib0052 article-title: Advances in functional and structural MR image analysis and implementation as FSL publication-title: Neuroimage – year: 1994 ident: bib0047 publication-title: Data from TCIA pancreas-CT – reference: . – year: 2016 ident: bib0001 article-title: TensorFlow: large-scale machine learning on heterogeneous distributed systems – year: 2017 ident: bib0053 article-title: Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations publication-title: Proceedings of MICCAI Workshop on Deep Learning in Medical Image Analysis (DLMIA) – volume: vol. 10135 year: 2017 ident: bib0024 article-title: Deep residual networks for automatic segmentation of laparoscopic videos of the liver publication-title: Proceedings of the SPIE, Medical Imaging – volume: 42 start-page: 60 year: 2017 end-page: 88 ident: bib0034 article-title: A survey on deep learning in medical image analysis publication-title: Medical Image Analysis – volume: 139 start-page: 181 year: 2017 end-page: 190 ident: bib0016 article-title: GIFT-Cloud: A data sharing and collaboration platform for medical imaging research publication-title: Comput. Methods Programs Biomed. – year: 1994 ident: bib0020 publication-title: Design Patterns: Elements of Reusable Object-Oriented Software – ident: 10.1016/j.cmpb.2018.01.025_bib0005 – start-page: 2135 year: 2016 ident: 10.1016/j.cmpb.2018.01.025_bib0050 article-title: CNTK: Microsoft’s open-source deep-learning toolkit – start-page: 111 year: 2016 ident: 10.1016/j.cmpb.2018.01.025_bib0057 article-title: Three-dimensional CT image segmentation by combining 2D fully convolutional network with 3D majority voting – volume: 139 start-page: 181 year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0016 article-title: GIFT-Cloud: A data sharing and collaboration platform for medical imaging research publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2016.11.004 – year: 2011 ident: 10.1016/j.cmpb.2018.01.025_bib0013 article-title: Torch7: A MATLAB-like environment for machine learning – year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0023 article-title: Towards image-guided pancreas and biliary endoscopy: automatic multi-organ segmentation on abdominal CT with dense dilated networks – year: 2014 ident: 10.1016/j.cmpb.2018.01.025_bib0048 article-title: A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations – volume: 23 start-page: S208 year: 2004 ident: 10.1016/j.cmpb.2018.01.025_bib0052 article-title: Advances in functional and structural MR image analysis and implementation as FSL publication-title: Neuroimage doi: 10.1016/j.neuroimage.2004.07.051 – ident: 10.1016/j.cmpb.2018.01.025_bib0015 – volume: 36 start-page: 61 year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0030 article-title: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation publication-title: Med. Image Anal. doi: 10.1016/j.media.2016.10.004 – volume: 29 start-page: 196 issue: 1 year: 2010 ident: 10.1016/j.cmpb.2018.01.025_bib0031 article-title: Elastix: a toolbox for intensity-based medical image registration publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2009.2035616 – volume: 34 start-page: 1993 issue: 10 year: 2015 ident: 10.1016/j.cmpb.2018.01.025_bib0039 article-title: The multimodal brain tumor image segmentation benchmark (BraTS) publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2014.2377694 – volume: 7 year: 2013 ident: 10.1016/j.cmpb.2018.01.025_bib0036 article-title: The design of SimpleITK publication-title: Front. Neuroinf. doi: 10.3389/fninf.2013.00045 – ident: 10.1016/j.cmpb.2018.01.025_bib0009 – volume: vol. 10135 year: 2017 ident: 10.1016/j.cmpb.2018.01.025_sbref0017 article-title: Deep residual networks for automatic segmentation of laparoscopic videos of the liver – start-page: 221 issue: 19 year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0051 article-title: Deep learning in medical image analysis publication-title: Ann. Rev. Biomed. Eng. doi: 10.1146/annurev-bioeng-071516-044442 – volume: 14 start-page: 711 issue: 4 year: 1995 ident: 10.1016/j.cmpb.2018.01.025_bib0035 article-title: Artificial convolution neural network techniques and applications for lung nodule detection publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.476112 – year: 1994 ident: 10.1016/j.cmpb.2018.01.025_bib0047 – volume: 10 start-page: 301 issue: 3 year: 2015 ident: 10.1016/j.cmpb.2018.01.025_bib0012 article-title: The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging publication-title: Int. J. Comput. Assist. Radiol. Surg. doi: 10.1007/s11548-014-1124-7 – ident: 10.1016/j.cmpb.2018.01.025_bib0037 – start-page: 424 year: 2016 ident: 10.1016/j.cmpb.2018.01.025_bib0011 article-title: 3D U-net: learning dense volumetric segmentation from sparse annotation – start-page: 348 year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0033 article-title: On the compactness, efficiency, and representation of 3D convolutional networks: Brain parcellation as a pretext task – year: 2012 ident: 10.1016/j.cmpb.2018.01.025_bib0008 article-title: NiftySeg: open-source software for medical image segmentation, label fusion and cortical thickness estimation – volume: 54 start-page: 2033 issue: 3 year: 2011 ident: 10.1016/j.cmpb.2018.01.025_bib0002 article-title: A reproducible evaluation of ANTs similarity metric performance in brain image registration publication-title: Neuroimage doi: 10.1016/j.neuroimage.2010.09.025 – volume: 33 start-page: 2332 issue: 12 year: 2014 ident: 10.1016/j.cmpb.2018.01.025_bib0007 article-title: Attenuation correction synthesis for hybrid PET-MR scanners: Application to brain studies publication-title: Med. Imaging IEEE Trans. doi: 10.1109/TMI.2014.2340135 – volume: 98 start-page: 278 issue: 3 year: 2010 ident: 10.1016/j.cmpb.2018.01.025_bib0042 article-title: Fast free-form deformation using graphics processing units publication-title: Comput. Methods Progr. Biomed. doi: 10.1016/j.cmpb.2009.09.002 – volume: 10 start-page: 1077 issue: 7 year: 2015 ident: 10.1016/j.cmpb.2018.01.025_bib0029 article-title: NiftySim: a GPU-based nonlinear finite element package for simulation of soft tissue biomechanics publication-title: Int. J. Comput. Assist. Radiol. Surg. doi: 10.1007/s11548-014-1118-5 – year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0053 article-title: Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations – start-page: 565 year: 2016 ident: 10.1016/j.cmpb.2018.01.025_bib0040 article-title: V-Net: Fully convolutional neural networks for volumetric medical image segmentation – ident: 10.1016/j.cmpb.2018.01.025_bib0006 – start-page: 675 year: 2014 ident: 10.1016/j.cmpb.2018.01.025_bib0028 article-title: Caffe: Convolutional architecture for fast feature embedding – ident: 10.1016/j.cmpb.2018.01.025_bib0055 – ident: 10.1016/j.cmpb.2018.01.025_bib0049 – volume: 42 start-page: 60 year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0034 article-title: A survey on deep learning in medical image analysis publication-title: Medical Image Analysis doi: 10.1016/j.media.2017.07.005 – year: 2017 ident: 10.1016/j.cmpb.2018.01.025_sbref0015 article-title: Automatic multi-organ segmentation on abdominal CT with dense v-networks publication-title: IEEE Trans. Med. Imaging – year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0017 – year: 1994 ident: 10.1016/j.cmpb.2018.01.025_bib0020 – ident: 10.1016/j.cmpb.2018.01.025_bib0032 – start-page: 698 year: 2006 ident: 10.1016/j.cmpb.2018.01.025_bib0044 article-title: The NA-MIC kit: ITK, VTK, pipelines, grids and 3D Slicer as an open platform for the medical image computing community – ident: 10.1016/j.cmpb.2018.01.025_bib0041 – year: 2016 ident: 10.1016/j.cmpb.2018.01.025_sbref0001 – ident: 10.1016/j.cmpb.2018.01.025_bib0045 – ident: 10.1016/j.cmpb.2018.01.025_bib0003 – start-page: 1942 year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0027 article-title: Batch renormalization: towards reducing minibatch dependence in batch-normalized models – volume: 9 start-page: 195 issue: 2 year: 1999 ident: 10.1016/j.cmpb.2018.01.025_bib0019 article-title: Cortical surface-based analysis: II: inflation, flattening, and a surface-based coordinate system publication-title: Neuroimage doi: 10.1006/nimg.1998.0396 – ident: 10.1016/j.cmpb.2018.01.025_bib0056 – year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0026 article-title: Freehand ultrasound image simulation with spatially-conditioned generative adversarial networks – volume: 8 start-page: 607 issue: 4 year: 2013 ident: 10.1016/j.cmpb.2018.01.025_bib0043 article-title: The medical imaging interaction toolkit: challenges and advances publication-title: Int. J. Comput. Assist. Radiol. Surg. doi: 10.1007/s11548-013-0840-8 – volume: 10135 year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0038 article-title: DeepInfer: Open-source deep learning deployment toolkit for image-guided therapy – volume: 9 start-page: 179 issue: 2 year: 1999 ident: 10.1016/j.cmpb.2018.01.025_bib0014 article-title: Cortical surface-based analysis:I. Segmentation and surface reconstruction publication-title: Neuroimage doi: 10.1006/nimg.1998.0395 – ident: 10.1016/j.cmpb.2018.01.025_bib0010 – ident: 10.1016/j.cmpb.2018.01.025_bib0018 – ident: 10.1016/j.cmpb.2018.01.025_bib0025 – ident: 10.1016/j.cmpb.2018.01.025_bib0046 – start-page: 5717 year: 2017 ident: 10.1016/j.cmpb.2018.01.025_bib0021 article-title: ToolNet: holistically-nested real-time segmentation of robotic surgical tools – year: 2015 ident: 10.1016/j.cmpb.2018.01.025_bib0054 article-title: MatConvNet – convolutional neural networks for MATLAB – year: 2012 ident: 10.1016/j.cmpb.2018.01.025_bib0004 article-title: Theano: new features and speed improvements
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Snippet	•An open-source platform is implemented based on TensorFlow APIs for deep learning in medical imaging domain.•A modular implementation of the typical medical... Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established... • An open-source platform is implemented based on TensorFlow APIs for deep learning in medical imaging domain. • A modular implementation of the typical...
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SubjectTerms	Abdomen - diagnostic imaging Brain - diagnostic imaging Computer Simulation Convolutional neural network Databases, Factual Deep learning Diagnostic Imaging - instrumentation Diagnostic Imaging - methods Generative adversarial network Humans Image Processing, Computer-Assisted - instrumentation Image Processing, Computer-Assisted - methods Image regression Machine Learning Magnetic Resonance Imaging Medical image analysis Neural Networks, Computer Segmentation Ultrasonography
Title	NiftyNet: a deep-learning platform for medical imaging
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