Journal of Medical Systems

, 42:226 | Cite as

Medical Image Analysis using Convolutional Neural Networks: A Review

  • Syed Muhammad Anwar
  • Muhammad MajidEmail author
  • Adnan Qayyum
  • Muhammad Awais
  • Majdi Alnowami
  • Muhammad Khurram Khan
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing


The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.


Convolutional neural network Computer aided diagnosis Segmentation Classification Medical image analysis 


  1. 1.
    Greenspan, H., van Ginneken, B., and Summers, R. M., Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5):1153–1159, 2016.CrossRefGoogle Scholar
  2. 2.
    Wang, G., A perspective on deep imaging. IEEE Access 4:8914–8924, 2016.CrossRefGoogle Scholar
  3. 3.
    Liu, Y., Cheng, H., Huang, J., Zhang, Y., Tang, X., Tian, J.-W., and Wang, Y., Computer aided diagnosis system for breast cancer based on color doppler flow imaging. J. Med. Syst. 36(6):3975–3982, 2012.PubMedCrossRefGoogle Scholar
  4. 4.
    Diao, X.-F., Zhang, X.-Y., Wang, T.-F., Chen, S.-P., Yang, Y., and Zhong, L., Highly sensitive computer aided diagnosis system for breast tumor based on color doppler flow images. J. Med. Syst. 35(5):801–809, 2011.PubMedCrossRefGoogle Scholar
  5. 5.
    Wan, J., Wang, D., Hoi, S. C. H., Wu, P., Zhu, J., Zhang, Y., and Li, J.: Deep learning for content-based image retrieval: A comprehensive study. In: Proceedings of the 22nd ACM international conference on Multimedia. ACM, pp. 157–166, 2014Google Scholar
  6. 6.
    Deng, L., Yu, D., et al., Deep learning: Methods and applications. Foundations and Trends®, in Signal Processing 7(3–4):197–387, 2014.CrossRefGoogle Scholar
  7. 7.
    Shi, S., Wang, Q., Xu, P., and Chu, X.: Benchmarking state-of-the-art deep learning software tools. In: 2016 7th International Conference on Cloud Computing and Big Data (CCBD). IEEE, pp. 99–104, 2016Google Scholar
  8. 8.
    Janowczyk, A., and Madabhushi, A.: Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases, Journal of pathology informatics 7Google Scholar
  9. 9.
    Lakhani, P., Gray, D. L., Pett, C. R., Nagy, P., and Shih, G., Hello world deep learning in medical imaging. J. Digit. Imaging 31(3):283–289, 2018.PubMedCentralCrossRefGoogle Scholar
  10. 10.
    Heidenreich, A., Desgrandschamps, F., and Terrier, F., Modern approach of diagnosis and management of acute flank pain: Review of all imaging modalities. Eur. Urol. 41(4):351–362, 2002.PubMedCrossRefGoogle Scholar
  11. 11.
    Rahman, M. M., Desai, B.C., and Bhattacharya, P., Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion. Comput. Med. Imaging Graph. 32(2):95–108, 2008.PubMedCrossRefGoogle Scholar
  12. 12.
    Sáez, A., Sánchez-Monedero, J., Gutiérrez, P. A., and Hervás-Martínez, C., Machine learning methods for binary and multiclass classification of melanoma thickness from dermoscopic images. IEEE Trans. Med. Imaging 35(4):1036–1045, 2016.PubMedCrossRefGoogle Scholar
  13. 13.
    Miri, M. S., Abràmoff, M. D., Lee, K., Niemeijer, M., Wang, J.-K., Kwon, Y. H., and Garvin, M. K., Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach. IEEE Trans. Med. Imaging 34(9):1854–1866, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Gao, Y., Zhan, Y., and Shen, D., Incremental learning with selective memory (ilsm): Towards fast prostate localization for image guided radiotherapy. IEEE Trans. Med. Imaging 33(2):518–534, 2014.PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Tao, Y., Peng, Z., Krishnan, A., and Zhou, X. S., Robust learning-based parsing and annotation of medical radiographs. IEEE Trans. Med. Imaging 30(2):338–350, 2011.PubMedCrossRefGoogle Scholar
  16. 16.
    Ahmad, J., Muhammad, K., Lee, M. Y., and Baik, S. W., Endoscopic image classification and retrieval using clustered convolutional features. J. Med. Syst. 41(12):196, 2017.PubMedCrossRefGoogle Scholar
  17. 17.
    Ahmad, J., Muhammad, K., and Baik, S. W., Medical image retrieval with compact binary codes generated in frequency domain using highly reactive convolutional features. J. Med. Syst. 42(2):24, 2018.CrossRefGoogle Scholar
  18. 18.
    Jenitta, A., and Ravindran, R. S., Image retrieval based on local mesh vector co-occurrence pattern for medical diagnosis from mri brain images. J. Med. Syst. 41(10):157, 2017.PubMedCrossRefGoogle Scholar
  19. 19.
    Zhang, L., and Ji, Q., A bayesian network model for automatic and interactive image segmentation. IEEE Trans. Image Process. 20(9):2582–2593, 2011.PubMedCrossRefGoogle Scholar
  20. 20.
    Sharma, M. M.: Brain tumor segmentation techniques: A survey. Brain 4 (4): 220–223Google Scholar
  21. 21.
    Vishnuvarthanan, G., Rajasekaran, M. P., Subbaraj, P., and Vishnuvarthanan, A., An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl. Soft Comput. 38:190–212, 2016.CrossRefGoogle Scholar
  22. 22.
    Feng, Y., Zhao, H., Li, X., Zhang, X., and Li, H., A multi-scale 3d otsu thresholding algorithm for medical image segmentation. Digital Signal Process. 60:186–199, 2017.CrossRefGoogle Scholar
  23. 23.
    Gupta, D., and Anand, R., A hybrid edge-based segmentation approach for ultrasound medical images. Biomed. Signal Process. Control 31:116–126, 2017.CrossRefGoogle Scholar
  24. 24.
    von Landesberger, T., Basgier, D., and Becker, M., Comparative local quality assessment of 3d medical image segmentations with focus on statistical shape model-based algorithms. IEEE Trans. Vis. Comput. Graph. 22 (12):2537–2549, 2016.CrossRefGoogle Scholar
  25. 25.
    Anwar, S., Yousaf, S., and Majid, M.: Brain timor segmentation on multimodal mri scans using emap algorithm. In: Engineering in medicine and biology soceity (EMBC), International Conference of the IEEE. IEEE, pp. 1-4, 2018Google Scholar
  26. 26.
    Cabria, I., and Gondra, I., Mri segmentation fusion for brain tumor detection. Information Fusion 36:1–9, 2017.CrossRefGoogle Scholar
  27. 27.
    Soulami, K. B., Saidi, M. N., and Tamtaoui, A.: A cad system for the detection of abnormalities in the mammograms using the metaheuristic algorithm particle swarm optimization (pso). In: Advances in Ubiquitous Networking 2. Springer, pp. 505–517, 2017Google Scholar
  28. 28.
    Kobayashi, Y., Kobayashi, H., Giles, J. T., Yokoe, I., Hirano, M., Nakajima, Y., and Takei, M., Detection of left ventricular regional dysfunction and myocardial abnormalities using complementary cardiac magnetic resonance imaging in patients with systemic sclerosis without cardiac symptoms: A pilot study. Intern. Med. 55(3): 237–243, 2016.PubMedCrossRefGoogle Scholar
  29. 29.
    Mosquera-Lopez, C., Agaian, S., Velez-Hoyos, A., and Thompson, I., Computer-aided prostate cancer diagnosis from digitized histopathology: A review on texture-based systems. IEEE Rev. Biomed. Eng. 8:98–113, 2015.PubMedCrossRefGoogle Scholar
  30. 30.
    Ma, H.-Y., Zhou, Z., Wu, S., Wan, Y.-L., and Tsui, P.-H., A computer-aided diagnosis scheme for detection of fatty liver in vivo based on ultrasound kurtosis imaging. J. Med. Syst. 40(1):33, 2016.PubMedCrossRefGoogle Scholar
  31. 31.
    Remeseiro, B., Mosquera, A., and Penedo, M. G., Casdes: A computer-aided system to support dry eye diagnosis based on tear film maps. IEEE journal of biomedical and health informatics 20(3):936–943, 2016.PubMedCrossRefGoogle Scholar
  32. 32.
    Torrents-Barrena, J., Lazar, P., Jayapathy, R., Rathnam, M., Mohandhas, B., and Puig, D., Complex wavelet algorithm for computer-aided diagnosis of alzheimer’s disease. Electron. Lett. 51(20):1566–1568, 2015.CrossRefGoogle Scholar
  33. 33.
    Saha, M., Mukherjee, R., and Chakraborty, C., Computer-aided diagnosis of breast cancer using cytological images: A systematic review. Tissue Cell 48(5):461–474, 2016.PubMedCrossRefGoogle Scholar
  34. 34.
    Salam, A. A., Akram, M. U., Wazir, K., Anwar, S. M., and Majid, M.: Autonomous glaucoma detection from fundus image using cup to disc ratio and hybrid features. In: IEEE International Symposium on Signal processing and information technology (ISSPIT) 2015. IEEE, pp. 370-374, 2015Google Scholar
  35. 35.
    Salam, A. A., Akram, M. U., Abbas, S., and Anwar, S. M.: Optic disc localization using local vessel based features and support vector machine. In: IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), 2015. IEEE, pp. 1–6, 2015Google Scholar
  36. 36.
    Altaf, T., Anwar, S. M., Gul, N., Majeed, M. N., and Majid, M., Multi-class alzheimer’s disease classification using image and clinical features. Biomed. Signal Process. Control 43:64–74, 2018.CrossRefGoogle Scholar
  37. 37.
    Hwang, K. H., Lee, H., and Choi, D., Medical image retrieval: Past and present. Healthcare informatics research 18(1):3–9, 2012.PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Müller, H., Rosset, A., Vallée, J.-P., Terrier, F., and Geissbuhler, A., A reference data set for the evaluation of medical image retrieval systems. Comput. Med. Imaging Graph. 28(6):295–305, 2004.PubMedCrossRefGoogle Scholar
  39. 39.
    Müller, H., Michoux, N., Bandon, D., and Geissbuhler, A., A review of content-based image retrieval systems in medical applications—clinical benefits and future directions. Int. J. Med. Inform. 73(1):1–23, 2004.PubMedCrossRefGoogle Scholar
  40. 40.
    Mizotin, M., Benois-Pineau, J., Allard, M., and Catheline, G.: Feature-based brain mri retrieval for alzheimer disease diagnosis. In: 2012 19th IEEE International Conference on Image Processing (ICIP). IEEE, pp. 1241–1244, 2012Google Scholar
  41. 41.
    Brahmi, D., and Ziou, D.: Improving cbir systems by integrating semantic features. In: 2004 Proceedings of the 1st Canadian Conference on Computer and robot vision. IEEE, pp. 233-240, 2004Google Scholar
  42. 42.
    Chang, N.-S., and Fu, K.-S., Query-by-pictorial-example. IEEE Trans. Softw. Eng. SE-6(6):519–524, 1980.CrossRefGoogle Scholar
  43. 43.
    Thakur, M. S., and Singh, M., Content based image retrieval using line edge singular value pattern (lesvp): A review paper. International Journal of Advanced Research in Computer Science and Software Engineering 5(3): 648–652, 2015.Google Scholar
  44. 44.
    Jiji, G. W., and Raj, P. S. J. D., Content-based image retrieval in dermatology using intelligent technique. IET Image Process. 9(4):306–317, 2014.CrossRefGoogle Scholar
  45. 45.
    Rahman, M. M., Antani, S. K., and Thoma, G. R., A learning-based similarity fusion and filtering approach for biomedical image retrieval using svm classification and relevance feedback. IEEE Trans. Inf. Technol. Biomed. 15(4):640–646, 2011.PubMedCrossRefGoogle Scholar
  46. 46.
    Anwar, S. M., Arshad, F., and Majid, M.: Fast wavelet based image characterization for content based medical image retrieval. In: 2017 International Conference on communication, computing and digital systems (C-CODE). IEEE, pp.351-356, 2017Google Scholar
  47. 47.
    Deng, L., Yu, D., et al., Deep learning: Methods and applications. Foundations and Trends®, in Signal Processing 7(3–4):197–387, 2014.CrossRefGoogle Scholar
  48. 48.
    Premaladha, J., and Ravichandran, K., Novel approaches for diagnosing melanoma skin lesions through supervised and deep learning algorithms. J. Med. Syst. 40(4):96, 2016.PubMedCrossRefGoogle Scholar
  49. 49.
    Kharazmi, P., Zheng, J., Lui, H., Wang, Z. J., and Lee, T. K., A computer-aided decision support system for detection and localization of cutaneous vasculature in dermoscopy images via deep feature learning. J. Med. Syst. 42(2):33, 2018.PubMedCrossRefGoogle Scholar
  50. 50.
    Wang, S.-H., Phillips, P., Sui, Y., Liu, B., Yang, M., and Cheng, H., Classification of alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J. Med. Syst. 42(5):85, 2018.PubMedCrossRefGoogle Scholar
  51. 51.
    LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P., Gradient-based learning applied to document recognition. Proc. IEEE 86(11):2278–2324, 1998.CrossRefGoogle Scholar
  52. 52.
    LeCun, Y., Bengio, Y., and Hinton, G., Deep learning. Nature 521(7553):436, 2015.CrossRefPubMedGoogle Scholar
  53. 53.
    Ding, S., Lin, L., Wang, G., and Chao, H., Deep feature learning with relative distance comparison for person re-identification. Pattern Recog. 48(10):2993–3003, 2015.CrossRefGoogle Scholar
  54. 54.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R., Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1):1929–1958, 2014.Google Scholar
  55. 55.
    Ioffe, S., and Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167
  56. 56.
    Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., and Karssemeijer, N., Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35:303–312, 2017. Scholar
  57. 57.
    Perez, L., and Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv:1712.04621
  58. 58.
    Hussain, S., Anwar, S. M., and Majid, M., Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing 282:248–261, 2018.CrossRefGoogle Scholar
  59. 59.
    Ma, J., Wu, F., Zhu, J., Xu, D., and Kong, D., A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics 73:221–230, 2017.PubMedCrossRefGoogle Scholar
  60. 60.
    Sun, W., Tseng, T.-L. B., Zhang, J., and Qian, W., Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput. Med. Imaging Graph. 57:4–9 , 2017.PubMedCrossRefGoogle Scholar
  61. 61.
    Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., and Zheng, Y., Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90:200–205, 2016.CrossRefGoogle Scholar
  62. 62.
    Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., and Mougiakakou, S., Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35 (5):1207–1216, 2016.PubMedCrossRefGoogle Scholar
  63. 63.
    van Tulder, G., and de Bruijne, M., Combining generative and discriminative representation learning for lung ct analysis with convolutional restricted boltzmann machines. IEEE Trans. Med. Imaging 35(5):1262–1272, 2016.PubMedCrossRefGoogle Scholar
  64. 64.
    Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., Metaxas, D. N., and Zhou, X. S., Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition. IEEE Trans. Med. Imaging 35(5):1332–1343, 2016.CrossRefGoogle Scholar
  65. 65.
    Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R., Cree, I. A., and Rajpoot, N. M., Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5):1196–1206, 2016.PubMedCrossRefGoogle Scholar
  66. 66.
    Qayyum, A., Anwar, S. M., Awais, M., and Majid, M., Medical image retrieval using deep convolutional neural network. Neurocomputing 266:8–20, 2017.CrossRefGoogle Scholar
  67. 67.
    Chowdhury, M., Bulo, S. R., Moreno, R., Kundu, M. K., and Smedby, Ö.: An efficient radiographic image retrieval system using convolutional neural network. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, pp. 3134–3139, 2016Google Scholar
  68. 68.
    Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., and Larochelle, H., Brain tumor segmentation with deep neural networks. Med. Image Anal. 35:18–31, 2017.PubMedCrossRefGoogle Scholar
  69. 69.
    Pereira, S., Pinto, A., Alves, V., and Silva, C. A., Brain tumor segmentation using convolutional neural networks in mri images. IEEE Trans. Med. Imaging 35(5):1240–1251 , 2016.PubMedCrossRefGoogle Scholar
  70. 70.
    Jodoin, A. C., Larochelle, H., Pal, C., and Bengio, Y.: Brain tumor segmentation with deep neural networksGoogle Scholar
  71. 71.
    Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., and Glocker, B., Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med. Image Anal. 36:61–78 , 2017.PubMedCrossRefGoogle Scholar
  72. 72.
    Tseng, K.-L., Lin, Y.-L., Hsu, W., and Huang, C.-Y.: Joint sequence learning and cross-modality convolution for 3d biomedical segmentation. arXiv:1704.07754
  73. 73.
    Casamitjana, A., Puch, S., Aduriz, A., Sayrol, E., and Vilaplana, V.: 3d convolutional networks for brain tumor segmentation. Proceedings of the MICCAI Challenge on Multimodal Brain Tumor Image Segmentation (BRATS), pp. 65–68 , 2016Google Scholar
  74. 74.
    Farooq, A., Anwar, S., Awais, M., and Rehman, S.: A deep cnn based multi-class classification of alzheimer’s disease using mri. In: 2017 IEEE International Conference on Imaging systems and techniques (IST). IEEE, pp. 1–6, 2017Google Scholar
  75. 75.
    Farooq, A., Anwar, S., Awais, M., and Alnowami, M.: Artificial intelligence based smart diagnosis of alzheimer’s disease and mild cognitive impairment. In: 2017 International Smart cities conference (ISC2). IEEE, pp. 1–4, 2017Google Scholar
  76. 76.
    Gangeh, M. J., Sørensen, L., Shaker, S. B., Kamel, M. S., De Bruijne, M., and Loog, M.: A texton-based approach for the classification of lung parenchyma in ct images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp. 595–602, 2010Google Scholar
  77. 77.
    Sorensen, L., Shaker, S. B., and De Bruijne, M., Quantitative analysis of pulmonary using local binary patterns. IEEE Trans. Med. Imaging 29(2):559–569, 2010.PubMedCrossRefGoogle Scholar
  78. 78.
    Anthimopoulos, M., Christodoulidis, S., Christe, A., and Mougiakakou, S.: Classification of interstitial lung disease patterns using local dct features and random forest. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp. 6040–6043, 2014Google Scholar
  79. 79.
    Chen, M., Shi, X., Zhang, Y., Wu, D., and Guizani, M.: Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Transactions on Big Data (1) 1–1., 2017
  80. 80.
    Hoo-Chang, S., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., and Summers, R. M., Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5):1285, 2016.PubMedCentralCrossRefGoogle Scholar
  81. 81.
    Simonyan, K., and Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
  82. 82.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., and Ronneberger, O.: 3D u-net Learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G., and Wells, W. (Eds.) Medical image computing and computer-assisted intervention – MICCAI, Vol. 2016, pp. 424–432. Springer International Publishing, Cham, 2016.CrossRefGoogle Scholar
  83. 83.
    Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, pp. 3–11, 2018Google Scholar
  84. 84.
    Chen, W., Zhang, Y., He, J., Qiao, Y., Chen, Y., Shi, H., and Tang, X.: W-net: Bridged u-net for 2d medical image segmentation. arXiv:1807.04459
  85. 85.
    Milletari, F., Navab, N., and Ahmadi, S.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571., 2016
  86. 86.
    LaLonde, R., and Bagci, U.: Capsules for object segmentation. arXiv:1804.04241
  87. 87.
    Chen, H., Dou, Q., Yu, L., and Heng, P.-A.: Voxresnet: Deep voxelwise residual networks for volumetric brain segmentation. arXiv:1608.05895
  88. 88.
    Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., Van Riel, S. J., Wille, M. M. W., Naqibullah, M., Sánchez, C. I., and van Ginneken, B., Pulmonary nodule detection in ct images: False positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35(5):1160–1169, 2016.PubMedCrossRefGoogle Scholar
  89. 89.
    Brosch, T., Tang, L. Y., Yoo, Y., Li, D. K., Traboulsee, A., and Tam, R., Deep 3d convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans. Med. Imaging 35(5):1229–1239, 2016.PubMedCrossRefGoogle Scholar
  90. 90.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., and Ronneberger, O.: 3d u-net: Learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp. 424–432, 2016Google Scholar
  91. 91.
    Ceschin, R., Zahner, A., Reynolds, W., Gaesser, J., Zuccoli, G., Lo, C. W., Gopalakrishnan, V., and Panigrahy, A., A computational framework for the detection of subcortical brain dysmaturation in neonatal mri using 3d convolutional neural networks. NeuroImage 178:183–197, 2018.PubMedCrossRefGoogle Scholar
  92. 92.
    Ghafoorian, M., Karssemeijer, N., Heskes, T., Bergkamp, M., Wissink, J., Obels, J., Keizer, K., de Leeuw, F.-E., van Ginneken, B., Marchiori, E., et al., Deep multi-scale location-aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin. NeuroImage: Clinical 14:391–399, 2017.CrossRefGoogle Scholar
  93. 93.
    Meijs, M., and Manniesing, R.: Artery and vein segmentation of the cerebral vasculature in 4d ct using a 3d fully convolutional neural network. In: Medical Imaging 2018: Computer-Aided Diagnosis, Vol. 10575, International Society for Optics and Photonics, p. 105751Q, 2018Google Scholar
  94. 94.
    Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., and Glocker, B., Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med. Image Anal. 36:61–78, 2017.PubMedCrossRefGoogle Scholar
  95. 95.
    Seong, S.-B., Pae, C., and Park, H.-J., Geometric convolutional neural network for analyzing surface-based neuroimaging data. Frontiers in Neuroinformatics 12:42, 2018.PubMedPubMedCentralCrossRefGoogle Scholar
  96. 96.
    Tzeng, E., Hoffman, J., Saenko, K., and Darrell, T.: Adversarial discriminative domain adaptation. In: Computer Vision and Pattern Recognition (CVPR), Vol. 1, p. 4, 2017Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Software EngineeringUniversity of Engineering and Technology TaxilaTaxilaPakistan
  2. 2.Department of Computer EngineeringUniversity of Engineering and Technology TaxilaTaxilaPakistan
  3. 3.Centre for Vision, Speech and Signal Processing (CVSSP)University of SurreyGuildfordUK
  4. 4.Department of Nuclear EngineeringKing Abdul Aziz UniversityJeddahSaudi Arabia
  5. 5.Center of Excellence in Information Assurance (CoEIA)King Saud UniversityRiyadhSaudi Arabia

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