1932

Abstract

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-bioeng-071516-044442
2017-06-21
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/bioeng/19/1/annurev-bioeng-071516-044442.html?itemId=/content/journals/10.1146/annurev-bioeng-071516-044442&mimeType=html&fmt=ahah

Literature Cited

  1. Brody H. 1.  2013. Medical imaging. Nature 502:S81 [Google Scholar]
  2. Shao Y, Gao Y, Guo Y, Shi Y, Yang X, Shen D. 2.  2014. Hierarchical lung field segmentation with joint shape and appearance sparse learning. IEEE Trans. Med. Imaging 33:1761–80 [Google Scholar]
  3. Wang L, Chen KC, Gao Y, Shi F, Liao S. 3.  et al. 2014. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization. Med. Phys. 41:043503 [Google Scholar]
  4. Yap PH, Zhang Y, Shen D. 4.  2016. Multi-tissue decomposition of diffusion MRI signals via L0 sparse-group estimation. IEEE Trans. Image Process. 25:4340–53 [Google Scholar]
  5. Suk HI, Lee SW, Shen D. 5.  2016. Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis. Brain Struct. Funct. 221:2569–87 [Google Scholar]
  6. Chen Y, Juttukonda M, Su Y, Benzinger T, Rubin BG. 6.  et al. 2015. Probabilistic air segmentation and sparse regression estimated pseudo CT for PET/MR attenuation correction. Radiology 275:562–69 [Google Scholar]
  7. Schmidhuber J. 7.  2015. Deep learning in neural networks: an overview. Neural Netw. 61:85–117 [Google Scholar]
  8. Bengio Y. 8.  2009. Learning Deep Architectures for AI: Foundations and Trends in Machine Learning Boston: Now127
  9. LeCun Y, Bengio Y, Hinton G. 9.  2015. Deep learning. Nature 521:436–44 [Google Scholar]
  10. Hinton GE, Salakhutdinov RR. 10.  2006. Reducing the dimensionality of data with neural networks. Science 313:504–7 [Google Scholar]
  11. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA. 11.  2010. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11:3371–408 [Google Scholar]
  12. Nair V, Hinton GE. 12.  2010. Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning807–14 New York: ACM
  13. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. 13.  2014. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15:1929–58 [Google Scholar]
  14. Ioffe S, Szegedy C. 14.  2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning448–56 New York: ACM
  15. Bishop CM. 15.  1995. Neural Networks for Pattern Recognition Oxford, UK: Oxford Univ. Press
  16. Collobert R, Weston J. 16.  2008. A unified architecture for natural language processing: deep neural networks with multitask learning. Proceedings of the 25th International Conference on Machine Learning160–67 New York: ACM
  17. Sutskever I, Martens J, Hinton GE. 17.  2011. Generating text with recurrent neural networks. Proceedings of the 28th International Conference on Machine Learning1017–24 New York: ACM
  18. Hinton GE, Deng L, Yu D, Dahl GE, Mohamed A. 18.  et al. 2012. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Proc. Mag. 29:82–97 [Google Scholar]
  19. Szegedy C, Toshev A, Erhan D. 19.  2013. Deep neural networks for object detection. Proceedings of the 26th Neural Information Processing Systems Conference (NIPS 2013) CJC Burges, L Bottou, M Welling, Z Ghahramani, KQ Weinberger 2553–61 https://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection [Google Scholar]
  20. Taigman Y, Yang M, Ranzato M, Wolf L. 20.  2014. DeepFace: closing the gap to human-level performance in face verification. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition1701–8 Washington, DC: IEEE
  21. Zhang J, Zong C. 21.  2015. Deep neural networks in machine translation: an overview. IEEE Intell. Syst. 30:16–25 [Google Scholar]
  22. Karpathy A, Li F. 22.  2015. Deep visual–semantic alignments for generating image descriptions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition3128–37 Washington, DC: IEEE
  23. Silver D, Huang A, Maddison CJ, Guez A, Sifre L. 23.  et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529:484–89 [Google Scholar]
  24. Russakovsky O, Deng J, Su H, Krause J, Satheesh S. 24.  et al. 2015. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115:211–52 [Google Scholar]
  25. Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A. 25.  2012. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) results http://host.robots.ox.ac.uk/pascal/VOC/voc2012/ [Google Scholar]
  26. Zhang W, Li R, Deng H, Wang L, Lin W. 26.  et al. 2015. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108:214–24 [Google Scholar]
  27. Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K. 27.  et al. 2016. Deep MRI brain extraction: a 3D convolutional neural network for skull stripping. NeuroImage 129:460–69 [Google Scholar]
  28. Wu G, Kim M, Wang Q, Munsell BC, Shen D. 28.  2016. Scalable high-performance image registration framework by unsupervised deep feature representations learning. IEEE Trans. Biomed. Eng. 63:1505–16 [Google Scholar]
  29. Suk HI, Lee SW, Shen D. 29.  2014. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101:569–82 [Google Scholar]
  30. Shin H, Roberts K, Lu L, Demner-Fushman D, Yao J, Summers RM. 30.  2016. Learning to read chest X-rays: recurrent neural cascade model for automated image annotation. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition2497–506 Washington, DC: IEEE
  31. Suk HI, Lee SW, Shen D. 31.  2015. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct. Funct. 220:841–59 [Google Scholar]
  32. Suk HI, Shen D. 32.  2015. Deep learning in diagnosis of brain disorders. Recent Progress in Brain and Cognitive Engineering SW Lee, HH Bülthoff, KR Müller 203–13 Berlin: Springer [Google Scholar]
  33. Suk HI, Wee CY, Lee SW, Shen D. 33.  2016. State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage 129:292–307 [Google Scholar]
  34. Pereira S, Pinto A, Alves V, Silva CA. 34.  2016. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35:1240–51 [Google Scholar]
  35. van Tulder G, de Bruijne M. 35.  2016. Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines. IEEE Trans. Med. Imaging 35:1262–72 [Google Scholar]
  36. Dou Q, Chen H, Yu L, Zhao L, Qin J. 36.  et al. 2016. Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans. Med. Imaging 35:1182–95 [Google Scholar]
  37. Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. 37.  2013. Mitosis detection in breast cancer histological images with deep neural networks. Proceedings of the 2013 Medical Image Computing and Computer-Assisted Intervention Conference411–18 Berlin: Springer
  38. Chen H, Dou Q, Wang X, Qin J, Heng PA. 38.  2016. Mitosis detection in breast cancer histology images via deep cascaded networks. Proceedings of the 30th AAAI Conference on Artificial Intelligence1167–73 Palo Alto, CA: AAAI
  39. Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM. 39.  et al. 2016. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep. 6:24454 [Google Scholar]
  40. Roth HR, Lu L, Liu J, Yao J, Seff A. 40.  et al. 2016. Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans. Med. Imaging 35:1170–81 [Google Scholar]
  41. Shen W, Zhou M, Yang F, Yang C, Tian J. 41.  2015. Multi-scale convolutional neural networks for lung nodule classification. Lecture Notes in Computer Science 9123 Information Processing in Medical Imaging588–99 Berlin: Springer [Google Scholar]
  42. Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C. 42.  et al. 2016. Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35:1160–69 [Google Scholar]
  43. Ciompi F, de Hoop B, van Riel SJ, Chung K, Scholten ET. 43.  et al. 2015. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Med. Image Anal. 26:195–202 [Google Scholar]
  44. Li R, Zhang W, Suk HI, Wang L, Li J. 44.  et al. 2014. Deep learning based imaging data completion for improved brain disease diagnosis. Proceedings of the 2014 Medical Image Computing and Computer-Assisted Intervention Conference305–12 Berlin: Springer
  45. Shin HC, Roth HR, Gao M, Lu L, Xu Z. 45.  et al. 2016. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35:1285–98 [Google Scholar]
  46. Gupta A, Ayhan M, Maida A. 46.  2013. Natural image bases to represent neuroimaging data. Proceedings of the 30th International Conference on Machine Learning987–94 New York: ACM
  47. Brosch T, Tam R. 47.  2013. Manifold learning of brain MRIs by deep learning. Proceedings of the 2013 Medical Image Computing and Computer-Assisted Intervention Conference633–40 Berlin: Springer
  48. Nie D, Wang L, Gao Y, Shen D. 48.  2016. Fully convolutional networks for multi-modality isointense infant brain image segmentation. Proceedings of the 13th IEEE International Symposium on Biomedical Imaging1342–45 Washington, DC: IEEE
  49. Brosch T, Tang LYW, Yoo Y, Li DKB, Traboulsee A, Tam R. 49.  2016. Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans. Med. Imaging 35:1229–39 [Google Scholar]
  50. Chen H, Dou Q, Wang X, Qin J, Heng P. 50.  2016. Mitosis detection in breast cancer histological images via deep cascaded networks. Proceedings of the 30th AAAI Conference on Artificial Intelligence1160–66 Palo Alto, CA: AAAI
  51. Shin HC, Orton MR, Collins DJ, Doran SJ, Leach MO. 51.  2013. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans. Pattern Anal. Mach. Intell. 35:1930–43 [Google Scholar]
  52. Wu G, Kim M, Wang Q, Gao Y, Liao S, Shen D. 52.  2013. Unsupervised deep feature learning for deformable registration of MR brain images. Proceedings of the 2013 Medical Image Computing and Computer-Assisted Intervention Conference649–56 Berlin: Springer
  53. Su H, Xing F, Kong X, Xie Y, Zhang S, Yang L. 53.  2015. Robust cell detection and segmentation in histopathological images using sparse reconstruction and stacked denoising autoencoders. Proceedings of the 2015 Medical Image Computing and Computer-Assisted Intervention Conference383–90 Berlin: Springer [Google Scholar]
  54. Xu J, Xiang L, Liu Q, Gilmore H, Wu J. 54.  et al. 2016. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35:119–30 [Google Scholar]
  55. Salakhutdinov R. 55.  2015. Learning deep generative models. Annu. Rev. Stat. Appl. 2:361–85 [Google Scholar]
  56. Munsell BC, Wee CY, Keller SS, Weber B, Elger C. 56.  et al. 2015. Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data. NeuroImage 118:219–30 [Google Scholar]
  57. Maier O, Schrder C, Forkert ND, Martinetz T, Handels H. 57.  2015. Classifiers for ischemic stroke lesion segmentation: a comparison study. PLOS ONE 10:1–16 [Google Scholar]
  58. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A. 58.  et al. 2017. Brain tumor segmentation with deep neural networks. Med. Image Anal. 35:18–31 [Google Scholar]
  59. Ronneberger O, Fischer P, Brox T. 59.  2015. U-net: convolutional networks for biomedical image segmentation. Proceedings of the 2015 Medical Image Computing and Computer-Assisted Intervention Conference234–41 Berlin: Springer
  60. Fakhry A, Peng H, Ji S. 60.  2016. Deep models for brain EM image segmentation: novel insights and improved performance. Bioinformatics 322352–58
  61. Farag A, Lu L, Roth HR, Liu J, Turkbey E, Summers RM. 61.  2015. A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling. arXiv:1505.06236 [cs.CV] [Google Scholar]
  62. Ghesu FC, Krubasik E, Georgescu B, Singh V, Zheng Y. 62.  et al. 2016. Marginal space deep learning: efficient architecture for volumetric image parsing. IEEE Trans. Med. Imaging 35:1217–28 [Google Scholar]
  63. Wang CW, Huang CT, Lee JH, Li CH, Chang SW. 63.  et al. 2016. A benchmark for comparison of dental radiography analysis algorithms. Med. Image Anal. 31:63–76 [Google Scholar]
  64. Rosenblatt F. 64.  1958. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 1958:65–386 [Google Scholar]
  65. Rumelhart DE, Hinton GE, Williams RJ. 65.  1986. Learning representations by back-propagating errors. Nature 323:533–36 [Google Scholar]
  66. Le QV, Ngiam J, Coates A, Lahiri A, Prochnow B, Ng AY. 66.  2011. On optimization methods for deep learning. Proceedings of the 28th International Conference on Machine Learning265–72 New York: ACM
  67. Hornik K. 67.  1991. Approximation capabilities of multilayer feedforward networks. Neural Netw. 4:251–57 [Google Scholar]
  68. Schwarz G. 68.  1978. Estimating the dimension of a model. Ann. Stat. 6:461–64 [Google Scholar]
  69. Bourlard H, Kamp Y. 69.  1988. Auto-association by multilayer perceptrons and singular value decomposition. Biol. Cybern. 59:291–94 [Google Scholar]
  70. Bengio Y, Lamblin P, Popovici D, Larochelle H. 70.  2007. Greedy layer-wise training of deep networks. Proceedings of the 19th Conference on Neural Information Processing Systems (NIPS 2006) B Schölkopf, JC Platt, T Hoffmann 153–60 https://papers.nips.cc/paper/3048-greedy-layer-wise-training-of-deep-networks [Google Scholar]
  71. Larochelle H, Bengio Y, Louradour J, Lamblin P. 71.  2009. Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 10:1–40 [Google Scholar]
  72. Hinton GE, Osindero S, Teh YW. 72.  2006. A fast learning algorithm for deep belief nets. Neural Comput. 18:1527–54 [Google Scholar]
  73. Smolensky P. 73.  1986. Information processing in dynamical systems: foundations of harmony theory. Parallel Distributed Processing: Explorations in the Microstructure of Cognition194–281 Cambridge, MA: MIT Press [Google Scholar]
  74. Hinton GE. 74.  2002. Training products of experts by minimizing contrastive divergence. Neural Comput. 14:1771–800 [Google Scholar]
  75. Hinton G, Dayan P, Frey B, Neal R. 75.  1995. The “wake–sleep” algorithm for unsupervised neural networks. Science 268:1158–61 [Google Scholar]
  76. Lecun Y, Bottou L, Bengio Y, Haffner P. 76.  1998. Gradient-based learning applied to document recognition. Proc. IEEE 86:2278–324 [Google Scholar]
  77. Glorot X, Bengio Y. 77.  2010. Understanding the difficulty of training deep feedforward neural networks. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics249–56 Brookline, MA: Microtome
  78. Sutskever I, Martens J, Dahl GE, Hinton GE. 78.  2013. On the importance of initialization and momentum in deep learning. Proceedings of the 28th International Conference on Machine Learning1139–47 New York: ACM
  79. Glorot X, Bordes A, Bengio Y. 79.  2011. Deep sparse rectifier neural networks. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics G Gordon, D Dunson, M Dudik 315–23 Brookline, MA: Microtome
  80. Maas AL, Hannun AY, Ng AY. 80.  2013. Rectifier nonlinearities improve neural network acoustic models. Proceedings of the 30th International Conference on Machine Learning, Workshop on Deep Learning for Audio, Speech, and Language Processing192 New York: ACM
  81. Wan L, Zeiler MD, Zhang S, LeCun Y, Fergus R. 81.  2013. Regularization of neural networks using DropConnect. Proceedings of the 30th International Conference on Machine Learning1056–66 New York: ACM
  82. Cho ZH, Kim YB, Han JY, Min HK, Kim KN. 82.  et al. 2008. New brain atlas—mapping the human brain in vivo with 7.0 T MRI and comparison with postmortem histology: Will these images change modern medicine?. Int. J. Imaging Syst. Technol. 18:2–8 [Google Scholar]
  83. Wu G, Qi F, Shen D. 83.  2006. Learning-based deformable registration of MR brain images. IEEE Trans. Med. Imaging 25:1145–57 [Google Scholar]
  84. Ou Y, Sotiras A, Paragios N, Davatzikos C. 84.  2011. DRAMMS: deformable registration via attribute matching and mutual-saliency weighting. Med. Image Anal. 15:622–39 [Google Scholar]
  85. Sotiras A, Davatzikos C, Paragios N. 85.  2013. Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32:1153–90 [Google Scholar]
  86. Lowe DG. 86.  1999. Object recognition from local scale-invariant features. Proceedings of the IEEE International Conference on Computer Vision8 http://www.cs.ubc.ca/∼lowe/papers/iccv99.pdf
  87. Vercauteren T, Pennec X, Perchant A, Ayache N. 87.  2009. Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45:S61–72 [Google Scholar]
  88. Wu G, Kim M, Wang Q, Shen D. 88.  2014. S-HAMMER: hierarchical attribute-guided, symmetric diffeomorphic registration for MR brain images. Hum. Brain Mapp. 35:1044–60 [Google Scholar]
  89. Liao S, Gao Y, Oto A, Shen D. 89.  2013. Representation learning: a unified deep learning framework for automatic prostate MR segmentation. Proceedings of the 2013 Medical Image Computing and Computer-Assisted Intervention Conference254–61 Berlin: Springer
  90. Guo Y, Gao Y, Shen D. 90.  2016. Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans. Med. Imaging 35:1077–89 [Google Scholar]
  91. Liao S, Gao Y, Shi Y, Yousuf A, Karademir I. 91.  et al. 2013. Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization. Inf. Proc. Med. Imaging 23:511–23 [Google Scholar]
  92. Kim M, Wu G, Shen D. 92.  2013. Unsupervised deep learning for hippocampus segmentation in 7.0 Tesla MR images. Lecture Notes in Computer Science 8184 Machine Learning in Medical Imaging1–8 Berlin: Springer [Google Scholar]
  93. Roth HR, Lee CT, Shin HC, Seff A, Kim L. 93.  et al. 2015. Anatomy-specific classification of medical images using deep convolutional nets. Proceedings of the IEEE 12th International Symposium on Biomedical Imaging293–303 Washington, DC: IEEE
  94. Yan Z, Zhan Y, Peng Z, Liao S, Shinagawa Y. 94.  et al. 2015. Bodypart recognition using multi-stage deep learning. Proceedings of the 24th Conference on Information Processing in Medical Imaging449–61 New York: ACM
  95. Yan Z, Zhan Y, Peng Z, Liao S, Shinagawa Y. 95.  et al. 2016. Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition. IEEE Trans. Med. Imaging 35:1332–43 [Google Scholar]
  96. Maron O, Lozano-Pérez T. 96.  1998. A framework for multiple-instance learning. Proceedings of Neural Information Processing Systems (NIPS 1998)570–76 https://papers.nips.cc/paper/1346-a-framework-for-multiple-instance-learning [Google Scholar]
  97. Liu F, Yang L. 97.  2015. A novel cell detection method using deep convolutional neural network and maximum-weight independent set. Proceedings of the 2015 Medical Image Computing and Computer-Assisted Intervention Conference349–57 Berlin: Springer
  98. Xie Y, Xing F, Kong X, Su H, Yang L. 98.  2015. Beyond classification: structured regression for robust cell detection using convolutional neural network. Proceedings of the 2015 Medical Image Computing and Computer-Assisted Intervention Conference358–65 Berlin: Springer
  99. Xie Y, Kong X, Xing F, Liu F, Su H, Yang L. 99.  2015. Deep voting: a robust approach toward nucleus localization in microscopy images. Proceedings of the 2015 Medical Image Computing and Computer-Assisted Intervention Conference374–82 Berlin: Springer
  100. Sirinukunwattana K, Raza SEA, Tsang YW, Snead DRJ, Cree IA, Rajpoot NM. 100.  2016. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35:1196–206 [Google Scholar]
  101. Long J, Shelhamer E, Darrell T. 101.  2015. Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition371–80 Washington, DC: IEEE
  102. Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJNL, Iśgum I. 102.  2016. Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35:1252–61 [Google Scholar]
  103. Weisenfeld NI, Warfield SK. 103.  2009. Automatic segmentation of newborn brain MRI. NeuroImage 47:564–72 [Google Scholar]
  104. Xue H, Srinivasan L, Jiang S, Rutherford M, Edwards AD. 104.  et al. 2007. Automatic segmentation and reconstruction of the cortex from neonatal MRI. NeuroImage 38:461–77 [Google Scholar]
  105. Gui L, Lisowski R, Faundez T, Hüppi PS, Lazeyras F, Kocher M. 105.  2012. Morphology-driven automatic segmentation of MR images of the neonatal brain. Med. Image Anal. 16:1565–79 [Google Scholar]
  106. Warfield S, Kaus M, Jolesz FA, Kikinis R. 106.  2000. Adaptive, template moderated, spatially varying statistical classification. Med. Image Anal. 4:43–55 [Google Scholar]
  107. Prastawa M, Gilmore JH, Lin W, Gerig G. 107.  2005. Automatic segmentation of MR images of the developing newborn brain. Med. Image Anal. 9:457–66 [Google Scholar]
  108. Wang L, Shi F, Lin W, Gilmore JH, Shen D. 108.  2011. Automatic segmentation of neonatal images using convex optimization and coupled level sets. NeuroImage 58:805–17 [Google Scholar]
  109. Wang L, Shi F, Li G, Gao Y, Lin W. 109.  et al. 2014. Segmentation of neonatal brain MR images using patch-driven level sets. NeuroImage 84:141–58 [Google Scholar]
  110. Wang L, Gao Y, Shi F, Li G, Gilmore JH. 110.  et al. 2015. Links: learning-based multi-source integration framework for segmentation of infant brain images. NeuroImage 108:160–72 [Google Scholar]
  111. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y. 111.  2013. OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229 [cs.CV] [Google Scholar]
  112. Gao M, Bagci U, Lu L, Wu A, Buty M. 112.  et al. 2016. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput. Methods Biomech. Biomed. Eng. 2016:1–6 [Google Scholar]
  113. Krizhevsky A, Sutskever I, Hinton GE. 113.  2012. Imagenet classification with deep convolutional neural networks. Proceedings of Neural Information Processing Systems (NIPS 2012)1097–105 https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf [Google Scholar]
  114. Krizhevsky A. 114.  2009. Learning multiple layers of features from tiny images Tech. rep., Dep. Comput. Sci., Univ. Toronto, Can.
  115. Simonyan K, Zisserman A. 115.  2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 [cs.CV] [Google Scholar]
  116. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S. 116.  et al. 2015. Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition1–9 Washington, DC: IEEE
  117. Lee CY, Xie S, Gallagher PW, Zhang Z, Tu Z. 117.  2015. Deeply-supervised nets. Proceedings of the 18th International Conference on Artificial Intelligence and Statistics562–70 Brookline, MA: Microtome
  118. Gönen M, Alpaydın E. 118.  2011. Multiple kernel learning algorithms. J. Mach. Learn. Res. 12:2211–68 [Google Scholar]
  119. Larochelle H, Bengio Y. 119.  2008. Classification using discriminative restricted Boltzmann machines. Proceedings of the 25th International Conference on Machine Learning536–43 New York: ACM
  120. Plis SM, Hjelm D, Salakhutdinov R, Allen EA, Bockholt HJ. 120.  et al. 2014. Deep learning for neuroimaging: a validation study. Front. Neurosci. 8:229 [Google Scholar]
  121. Kim J, Calhoun VD, Shim E, Lee JH. 121.  2016. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. NeuroImage 124:127–46 [Google Scholar]
/content/journals/10.1146/annurev-bioeng-071516-044442
Loading
/content/journals/10.1146/annurev-bioeng-071516-044442
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error