NiftyNet: a deep-learning platform for medical imaging

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:1709.02764.

[6]

M. Brett, M. Hanke, B. Cipollini, M.-A. Côté, C. Markiewicz, S. Gerhard, E. Larson, Nibabel, 2016, Online. doi:10.5281/zenodo.60808.

[7]

Burgos "Attenuation correction synthesis for hybrid PET-MR scanners: Application to brain studies" Med. Imaging IEEE Trans. (2014) 10.1109/tmi.2014.2340135

[8]

Cardoso "NiftySeg: open-source software for medical image segmentation, label fusion and cortical thickness estimation" (2012)

[9]

S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, E. Shelhamer, cuDNN: efficient primitives for deep learning, arXiv:1410.0759v3.

[10]

F. Chollet, et al., Keras, 2015, https://github.com/fchollet/keras.

[11]

Çiçek "3D U-net: learning dense volumetric segmentation from sparse annotation" (2016)

[12]

Clarkson "The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging" Int. J. Comput. Assist. Radiol. Surg. (2015) 10.1007/s11548-014-1124-7

[13]

Collobert "Torch7: A MATLAB-like environment for machine learning" (2011)

[14]

Cortical Surface-Based Analysis

Anders M. Dale, Bruce Fischl, Martin I. Sereno

NeuroImage 1999 10.1006/nimg.1998.0395

[15]

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

[16]

Doel "GIFT-Cloud: A data sharing and collaboration platform for medical imaging research" Comput. Methods Programs Biomed. (2017) 10.1016/j.cmpb.2016.11.004

[17]

Dong (2017)

[18]

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:1707.00478.

[19]

Fischl "Cortical surface-based analysis: II: inflation, flattening, and a surface-based coordinate system" Neuroimage (1999) 10.1006/nimg.1998.0396

[20]

Gamma (1994)

[21]

Garcia-Peraza-Herrera "ToolNet: holistically-nested real-time segmentation of robotic surgical tools" (2017)

[22]

Gibson "Automatic multi-organ segmentation on abdominal CT with dense v-networks" IEEE Trans. Med. Imaging (2017)

[23]

Gibson "Towards image-guided pancreas and biliary endoscopy: automatic multi-organ segmentation on abdominal CT with dense dilated networks" (2017)

[24]

Gibson "Deep residual networks for automatic segmentation of laparoscopic videos of the liver" (2017)

[25]

I. Goodfellow, NIPS 2016 tutorial: generative adversarial networks (2016). arXiv:1701.00160v4.

[26]

Hu "Freehand ultrasound image simulation with spatially-conditioned generative adversarial networks" (2017)

[27]

Ioffe "Batch renormalization: towards reducing minibatch dependence in batch-normalized models" (2017)

[28]

Jia "Caffe: Convolutional architecture for fast feature embedding" (2014)

[29]

Johnsen "NiftySim: a GPU-based nonlinear finite element package for simulation of soft tissue biomechanics" Int. J. Comput. Assist. Radiol. Surg. (2015) 10.1007/s11548-014-1118-5

[30]

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

Konstantinos Kamnitsas, Christian Ledig, Virginia F.J. Newcombe et al.

Medical Image Analysis 2017 10.1016/j.media.2016.10.004

[31]

Klein "Elastix: a toolbox for intensity-based medical image registration" IEEE Trans. Med. Imaging (2010) 10.1109/tmi.2009.2035616

[32]

B. Landman, Z. Xu, J.E. Igelsias, M. Styner, T.R. Langerak, A. Klein, Multi-atlas labeling beyond the cranial vault, 2015, URL: https://www.synapse.org/#!Synapse:syn3193805, accessed July 2017. 10.7303/syn3193805.

[33]

Li "On the compactness, efficiency, and representation of 3D convolutional networks: Brain parcellation as a pretext task" (2017)

[34]

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi et al.

Medical Image Analysis 2017 10.1016/j.media.2017.07.005

[35]

Artificial convolution neural network techniques and applications for lung nodule detection

S.-C.B. Lo, S.-L.A. Lou, Jyh-Shyan Lin et al.

IEEE Transactions on Medical Imaging 1995 10.1109/42.476112

[36]

Lowekamp "The design of SimpleITK" Front. Neuroinf. (2013) 10.3389/fninf.2013.00045

[37]

D. Mané, et al., TensorBoard: TensorFlow’s visualization toolkit, 2015, https://github.com/tensorflow/tensorboard.

[38]

Mehrtash "DeepInfer: Open-source deep learning deployment toolkit for image-guided therapy" (2017)

[39]

Menze "The multimodal brain tumor image segmentation benchmark (BraTS)" IEEE Trans. Med. Imaging (2015) 10.1109/tmi.2014.2377694

[40]

Milletari "V-Net: Fully convolutional neural networks for volumetric medical image segmentation" (2016)

[41]

M. Mirza, S. Osindero, Conditional generative adversarial nets. Proc. NIPS 2016 Workshop on Adversarial Training. 2016 (2014), arXiv:1411.1784.

[42]

Modat "Fast free-form deformation using graphics processing units" Comput. Methods Progr. Biomed. (2010) 10.1016/j.cmpb.2009.09.002

[43]

Nolden "The medical imaging interaction toolkit: challenges and advances" Int. J. Comput. Assist. Radiol. Surg. (2013) 10.1007/s11548-013-0840-8

[44]

Pieper "The NA-MIC kit: ITK, VTK, pipelines, grids and 3D Slicer as an open platform for the medical image computing community" (2006)

[45]

T. Preston-Werner, Semantic versioning, Technical Report, T. Preston-Werner, 2015. URL: http://semver.org/.

[46]

M. Reynolds, et al., Sonnet, 2017, https://github.com/deepmind/sonnet.

[47]

Roth (1994)

[48]

Roth "A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations" (2014)

[49]

T. Salimans, D.P. Kingma, Weight normalization: a simple reparameterization to accelerate training of deep neural networks (2016). arXiv:1602.07868v3.

[50]

Seide "CNTK: Microsoft’s open-source deep-learning toolkit" (2016)

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Details

Published: May 01, 2018
Vol/Issue: 158
Pages: 113-122
License: View

Authors

Arizona State University, Mesa, Arizona

School of Biomedical Engineering & Imaging Sciences, King’s College London, London, U.K

Cite This Article

Eli Gibson, Wenqi Li, Carole Sudre, et al. (2018). NiftyNet: a deep-learning platform for medical imaging. Computer Methods and Programs in Biomedicine, 158, 113-122. https://doi.org/10.1016/j.cmpb.2018.01.025

NiftyNet: a deep-learning platform for medical imaging

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