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Medical Image Analysis Using Deep Learning: A Systematic Literature Review

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Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics (ICETCE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 985))

Abstract

The field of big data analytics has started playing a vital role in the advancement of Medical Image Analysis (MIA) over the last decades very quickly. Healthcare is a major example of how the three Vs of data i.e., velocity, variety, and volume, are an important feature of the data it generates. In medical imaging (MI), the exact diagnosis of the disease and/or assessment of disease relies on both image collection and interpretation. Image interpretation by the human experts is a bit difficult with respect to its discrimination, the complication of the image, and also the prevalent variations exist across various analyzers. Recent improvements in Machine Learning (ML), specifically in Deep Learning (DL), help in identifying, classifying and measuring patterns in medical images. This paper is focused on the Systematic Literature Review (SLR) of various microservice events like image localization, segmentation, detection, and classification tasks.

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References

  1. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  2. Qayyum, A., Anwar, S.M., Majid, M., Awais, M., Alnowami, M.: Medical image analysis using convolutional neural networks: a review. Comp Vis. Pattern Recogn. ArXiv: 1709.02250 (2017)

  3. Suzuki, K.: Overview of deep learning in medical imaging. Radiol. Phys. Technol. 10, 257 (2017)

    Article  Google Scholar 

  4. Shortliffe, E.H.: Computer-Based Medical Consultations: MYCIN, vol. 2. Elsevier, New York (1976)

    Google Scholar 

  5. Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)

    Article  Google Scholar 

  6. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 5(4), 115–133 (1943)

    MathSciNet  MATH  Google Scholar 

  7. Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2018)

    Article  Google Scholar 

  8. Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 365–386 (1958)

    Article  Google Scholar 

  9. Fukushima, K., Miyake, S.: Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. In: Amari, S., Arbib, M.A. (eds.) Competition and Cooperation in Neural Nets. LNBM, vol. 45, pp. 267–285. Springer, Berlin (1982). https://doi.org/10.1007/978-3-642-46466-9_18

    Chapter  Google Scholar 

  10. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Article  Google Scholar 

  11. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  Google Scholar 

  12. Digital Imaging and Communications in Medicine (DICOM). https://www.dicomstandard.org/

  13. Health Level Seven (HL7). http://www.hl7.org/index.cfm

  14. Integrating the Healthcare Enterprise (IHE). http://www.ihe.net/

  15. Picture Archiving and Communications Systems (PACS). http://www.pacshistory.org/index.html

  16. Dallora, A.L., Eivazzadeh, S., Mendes, E., Berglund, J., Anderberg, P.: Prognosis of dementia employing machine learning and microsimulation techniques: a systematic literature review. Procedia Comput. Sci. 100, 4808 (2016)

    Article  Google Scholar 

  17. Pai, M., McCulloch, M., Gorman, J.D., Pai, N., Enanoria, W., Kennedy, G., et al.: Systematic reviews and metaanalyses: an illustrated, step-by-step guide. Natl. Med. J. India 17(2), 8695 (2004). PMID 15141602

    Google Scholar 

  18. Sharma, K., Mediratta, P.: Importance of keywords for retrieval of relevant articles in medline search. Indian J. Pharm. 34, 369–371 (2002)

    Google Scholar 

  19. Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G.: The PRISMA group: preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6(7), e1000097 (2009). https://doi.org/10.1371/journal.pmed1000097

    Article  Google Scholar 

  20. Vaswani, S., Thota, R., Vydyanathan, N., Kale, A.: Fast 3D structure localization in medical volumes using CUDA-enabled GPUs. In: 2nd IEEE International Conference on Parallel, Distributed and Grid Computing, Solan, pp. 614–620 (2012)

    Google Scholar 

  21. Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_51

    Chapter  Google Scholar 

  22. Shin, H.C., Orton, M.R., Collins, D.J., Doran, S.J., Leach, M.O.: Stacked autoencoder 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 (2013)

    Article  Google Scholar 

  23. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., Le Cun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks. ArXiv: 1312.6229 (2014)

  24. Zheng, Y., Liu, D., Georgescu, B., Nguyen, H., Comaniciu, D.: 3D deep learning for efficient and robust landmark detection in volumetric data. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 565–572. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_69

    Chapter  Google Scholar 

  25. Chen, H., et al.: Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE J. Biomed. Health Inform. 19(5), 1627–1636 (2015)

    Article  Google Scholar 

  26. Chen, H., et al.: Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 507–514. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_62

    Chapter  Google Scholar 

  27. Su, H., Xing, F., Kong, X., Xie, Y., Zhang, S., Yang, L.: Robust cell detection and segmentation in histopathological images using sparse reconstruction and stacked denoising autoencoders. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 383–390. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_46

    Chapter  Google Scholar 

  28. De Vos, B.D., Wolterink, J.M., de Jong, P.A, Viergever M.A., Isgum I.: 2D image classification for 3D anatomy localization: employing deep convolutional neural networks. In: Medical Imaging, Proceedings of the SPIE, vol. 9784, p. 97841Y (2016)

    Google Scholar 

  29. Cai, Y., Landis, M., Laidley, D.T., Kornecki, A., Lum, A., Li, S.: Multi-modal vertebrae recognition using transformed deep convolution network. Comput. Med. Imaging Graph. 51, 11–19 (2016)

    Article  Google Scholar 

  30. Kumar, A., et al.: Plane identification in fetal ultrasound images using saliency maps and convolutional neural networks. In: IEEE International Symposium on Biomedical Imaging, pp. 791–794 (2016)

    Google Scholar 

  31. Payer, C., Štern, D., Bischof, H., Urschler, M.: Regressing heatmaps for multiple landmark localization using CNNs. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 230–238. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_27

    Chapter  Google Scholar 

  32. Ghesu, F.C., Georgescu, B., Mansi, T., Neumann, D., Hornegger, J., Comaniciu, D.: An artificial agent for anatomical landmark detection in medical images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 229–237. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46726-9_27

    Chapter  Google Scholar 

  33. Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R.J., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35, 1196–206 (2016)

    Article  Google Scholar 

  34. Liu, F., Yang, L.: A novel cell detection method using deep convolutional neural network and maximum-weight independent set. In: Lu, L., Zheng, Y., Carneiro, G., Yang, L. (eds.) Deep Learning and Convolutional Neural Networks for Medical Image Computing. ACVPR, pp. 63–72. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-42999-1_5

    Chapter  Google Scholar 

  35. Trebeschi, S., van Griethuysen, J.J.M., Lambregts, D.M.J., et al.: Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Sci. Rep. 7, 5301 (2017)

    Article  Google Scholar 

  36. Humpire Mamani, G.E., Setio, A.A.A., van Ginneken, B., Jacobs, C.: Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans. Phys. Med. Biol. 63(8), 085003 (2018)

    Article  Google Scholar 

  37. Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Proceedings of Advances in Neural Information Processing Systems, pp. 2843–2851 (2012)

    Google Scholar 

  38. Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B., Wang, T.: Accurate segmentation of cervical cytoplasm and nuclei based on multi-scale convolutional network and graph partitioning. IEEE Trans. Biomed. Eng. 10, 2421–2433 (2016)

    Google Scholar 

  39. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49

    Chapter  Google Scholar 

  40. Xie, Y., Zhang, Z., Sapkota, M., Yang, L.: Spatial clockwork recurrent neural network for muscle perimysium segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 185–193. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_22

    Chapter  Google Scholar 

  41. Poudel, R.P.K., Lamata, P, Montana, G.: Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. ArXiv: 1608.03974 (2016)

  42. Moeskops, P., et al.: Deep learning for multi-task medical image segmentation in multiple modalities. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 478–486. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_55

    Chapter  Google Scholar 

  43. Lo, S.-C., Lou, S.-L., Lin, J.-S., Freedman, M.T., Chien, M.V., Mun, S.K.: Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans. Med. Imaging 14, 711–718 (1995)

    Article  Google Scholar 

  44. Roth, H.R., et al.: Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans. Med. Imaging 35(5), 1170–1181 (2016)

    Article  Google Scholar 

  45. Teramoto, A., Fujita, H., Yamamuro, O., Tamaki, T.: Automated detection of pulmonary nodules in PET/CT images: ensemble false-positive reduction using a convolutional neural network technique. Med. Phys. 43, 2821–2827 (2016)

    Article  Google Scholar 

  46. van Grinsven, M.J.J.P., Ginneken, V., Hoyng, C., Theelen, B., Sanchez, C.: Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016)

    Article  Google Scholar 

  47. Setio, A.A., et al.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35(5), 1160–1169 (2016)

    Article  Google Scholar 

  48. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)

    Article  Google Scholar 

  49. Suk, H.-I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 583–590. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_72

    Chapter  Google Scholar 

  50. Suk, H.I., Lee, S.W., Shen, D.: Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101, 569–582 (2014)

    Article  Google Scholar 

  51. Hosseini Asl, E., Gimelfarb, G., El-Baz, A.: Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional network. arXiv: 1607.00556 (2016)

  52. Kawahara, J., et al.: BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146, 1038–1049 (2017)

    Article  Google Scholar 

  53. Lee, J.G., et al.: Deep learning in medical imaging: general overview. Korean J. Radiol. 4, 570–584 (2018)

    Google Scholar 

  54. Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19, 221–248 (2017)

    Article  Google Scholar 

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Sudheer Kumar, E., Shoba Bindu, C. (2019). Medical Image Analysis Using Deep Learning: A Systematic Literature Review. In: Somani, A., Ramakrishna, S., Chaudhary, A., Choudhary, C., Agarwal, B. (eds) Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics. ICETCE 2019. Communications in Computer and Information Science, vol 985. Springer, Singapore. https://doi.org/10.1007/978-981-13-8300-7_8

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  • DOI: https://doi.org/10.1007/978-981-13-8300-7_8

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