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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
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)
Suzuki, K.: Overview of deep learning in medical imaging. Radiol. Phys. Technol. 10, 257 (2017)
Shortliffe, E.H.: Computer-Based Medical Consultations: MYCIN, vol. 2. Elsevier, New York (1976)
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)
McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 5(4), 115–133 (1943)
Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2018)
Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 365–386 (1958)
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
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Digital Imaging and Communications in Medicine (DICOM). https://www.dicomstandard.org/
Health Level Seven (HL7). http://www.hl7.org/index.cfm
Integrating the Healthcare Enterprise (IHE). http://www.ihe.net/
Picture Archiving and Communications Systems (PACS). http://www.pacshistory.org/index.html
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)
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
Sharma, K., Mediratta, P.: Importance of keywords for retrieval of relevant articles in medline search. Indian J. Pharm. 34, 369–371 (2002)
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
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)
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
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)
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)
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
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)
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
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
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)
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)
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)
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
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
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)
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
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)
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)
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)
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)
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
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
Poudel, R.P.K., Lamata, P, Montana, G.: Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. ArXiv: 1608.03974 (2016)
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
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)
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)
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)
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)
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)
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)
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
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)
Hosseini Asl, E., Gimelfarb, G., El-Baz, A.: Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional network. arXiv: 1607.00556 (2016)
Kawahara, J., et al.: BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146, 1038–1049 (2017)
Lee, J.G., et al.: Deep learning in medical imaging: general overview. Korean J. Radiol. 4, 570–584 (2018)
Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19, 221–248 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-13-8300-7_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8299-4
Online ISBN: 978-981-13-8300-7
eBook Packages: Computer ScienceComputer Science (R0)