Abstract
Medical domain is of paramount importance and perhaps it is a high priority sector. A lot of concern is still required in this domain. Indeed, in majority of cases, the medical data is interpreted by human expert while analysis of medical data is quite strenuous and complicated task. Often, discrete analysis is performed by different human experts which results in inaccurate detection of disease. The excellent performance of deep learning (DL) in different realms attracted the researchers to apply this technique within the purview of medical domain as DL provides great precision and accuracy in final output. Therefore, it has been visualized as a core technique for medical image analysis and in other streams of the healthcare sector. It is an obvious fact that in medical image engineering (MIE), image analysis plays the vital role. Further, segmentation process is the fundamental, effective, and core step of the medical image analysis (MIA). Researchers are consistently striving to augment the accuracy of medical image analysis. In recent past, machine intelligence-based techniques have widely been used for this pursuit. The recent trend in the realm of medical image analysis is the implication of deep learning-based approaches. Perhaps, the application of deep learning enhances the predictive accuracies of the human being. Moreover, it also mitigates the intervention of human experts in the diagnosis phenomenon. In this chapter, authors prelude the rudiments of medical imaging as well as architectural view of neural network and DL. Moreover, different flamboyant features of DL over machine learning (ML) within the sphere of MIE are also critically analyzed. In addition, this chapter also includes the structural nuances of convolutional neural network (CNN). Furthermore, in this quest, the topic that “will deep learning be pragmatic in medical imaging?” is also discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bankman I (2009) Handbook of medical image processing and analysis, 2nd ed. Elsevier, pp 71–257 (2009)
Melonakos J (2009) Geodesic tractography segmentation for directional medical image analysis. PhD dissertation, Department of Electrical and Computer Engineering, Georgia Institute of Technology University (2009)
Harris G, Andreasen NC, Cizadlo T, Bailey JM, Bockholt HJ, Magnotta VA, Arndt S (1999) Improving tissue classification in MRI: a three-dimensional multispectral discriminant analysis method with automated training class selection. J Comput Assist Tomogr 23(1):144–154
Pham DL, Xu C, Princo JL (2000) A survey on current methods in medical image segmentation. Annu Rev Biomed Eng 2:315–337
Yu-Jin Z (2006) Advances in image and video segmentation. IEEE IRM Press
Zhang YJ (2002) Image engineering and related publications. Int J Image Graphics 2(3):441–452
Clarke LP, Velthuizen RP, Camacho MA, Heine JJ, Vaidyanathan M, Hall LO, Thatcher RW, Silbiger ML (1995) MRI segmentation: methods and applications. Magn Reson Imag 13:343–368
Leondes CT (2005) Medical imaging systems technology: methods in cardiovascular and brain. Singapore
Yang D, Zheng J, Nofal A, Deasy J, Naqa IME (2009) Techniques and software tool for 3D multimodality medical image segmentation. J Radiat Oncol Inform 1(1)
Olabarriag SD, Smeulders AWM (2001) Interaction in the segmentation of medical images: a survey. Med Image Anal (Elsevier) 127–142
Withey DJ, Koles ZJ (2007) Three generations of medical image segmentation-methods, available software. Int J Bioelectromagnetism 9(2)
Pitiot A, Delingette H, Thompson PM (2005) Automated image segmentation: issues and applications. In: Leondes CT (ed) Chapter 6 of Medical imaging systems technology: Methods in general anatomy, vol 3, pp 195–241
Sharma N, Aggarwal LM (2010) Automated medical image segmentation techniques. J Med Phys 3–14 [Online]. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2825001/
Nayak H, Amini MM, Bibalan PT, Bacon N (2009) Medical image segmentation-report
Neufeld E, Samaras T, Chavannes N, Kuster N (2006) Robust highly detailed medical image segmentation [Online]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.116.4080&rep=rep1&type=pdf
Nayak H, Amini MM, Bibalan PT, Bacon N (2006) Medical image segmentation-report
Souplet JC (2007) Medical image navigation and research tool by INRIA (MedINRIA)
Pan Z, Lu J (2007) A Bayes-based region-growing algorithm for medical image segmentation. IEEE Trans 9(4):32–38
Chartrand G, Cheng PM, Vorontsov E et al (2017) Deep learning: a primer for radiologists. Radiographics 37:2113–2131
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems, vol 1, Lake Tahoe, Nevada. Curran Associates Inc., pp 1097–1105
Ciompi F, de Hoop B, van Riel SJ 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
Nibali A, He Z, Wollersheim D (2017) Pulmonary nodule classification with deep residual networks. Int J Comput Assist Radiol Surg 12:1799–1808
Song Q, Zhao L, Luo X et al (2017) Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng 8314–740
Ciompi F, Chung K, van Riel SJ et al (2017) Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep 7:464–479
Hua KL, Hsu CH, Hidayati SC et al (2015) Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther 8:2015–2022
Sun W, Zheng B, Qian W (2017) Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med 89–530
Wang H, Zhao T, Li LC et al (2017) A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation. J Xray Sci Technol
Wang C, Elazab A, Wu J et al (2017) Lung nodule classification using deep feature fusion in chest radiography. Comput Med Imaging Graph 57:10–18
Setio AA, Traverso A, de Bel T et al (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med Image Anal 42:1–13
Anthimopoulos M, Christodoulidis S, Ebner L et al (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35:1207–1216
Wang H, Zhou Z, Li Y et al (2017) Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from (18)F-FDG PET/CT images. EJNMMI Res 7–11
Kohli M, Prevedello LM, Filice RW et al (2017) Implementing machine learning in radiology practice and research. AJR Am J Roentgenol 208:754–760
Dreyer KJ, Geis JR (2017) When machines think: radiology’s next frontier. Radiology 285:713–718
Clevert D-A, Unterthiner T, Hochreiter S (2015) Fast and accurate deep network learning by exponential linear units (ELUs). arXiv:1511.07289
He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on image net classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034
Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M (2014) Striving for simplicity: the all convolutional net. arXiv:1412.6806
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from over-fitting. J Mach Learn Res 15:1929–1958
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pp 448–456
LeCun Y, Bottou L, Bengio Y, Hanner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., pp 1097–1105
Lundervold AS, Lundervold A (2018) An overview of deep learning in medical imaging focusing on MRI. Zeitschrift fur medizinische Physik. https://doi.org/10.1016/j.zemedi.2018.11.00
Cui Z, Yang J, Qiao Y (2016) Brain MRI segmentation with patch-based CNN approach. In: IEEE control conference (CCC), pp 7026–7031
Razzak MI, Naz S, Zaib A (2017) Deep learning for medical image processing: overview, challenges and the future. Lect Notes Comput Vis Biomechanic (Springer) 26:323–350
Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G, Eaton-Rosen Z, Gray R, Doel T, Hu Y, Whyntie T, Nachev P, Modat M, Barratt DC, Ourselin S, Cardoso MJ, Vercauteren T (2018) NiftyNet: a deep-learning platform for medical imaging. Comput Methods Programs Biomed 158:113–122
Li W, Wang G, Fidon L, Ourselin S, Cardoso MJ, Vercauteren T (2017) On the compactness, efficiency, and representation of 3d convolutional networks: brain parcellation as a pretext task. In: International conference on information processing in medical imaging (IPMI)
Pawlowski N, Ktena SI, Lee MC, Kainz B, Rueckert D, Glocker B, Rajchl M (2017) DLTK: state of the art reference implementations for deep learning on medical images. arXiv:1711.06853
Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61–78
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention (MICCAI). LNCS, vol 9351. Springer, pp 234–241, arXiv:1505.04597 [cs.CV]
https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net
Milletari F, Navab N, Ahmadi S-A (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Fourth international conference on 3D vision (3DV). IEEE, pp 565–571
Badrinarayanan V, Kendall A, Cipolla R (2017) Seg-Net: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Machine Intelligence
Shin H-C, Tenenholtz NA, Rogers JK, Schwarz CG, Senjem ML, Gunter JL, Andriole KP, Michalski M (2018) Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: International workshop on simulation and synthesis in medical imaging. Springer, pp 1–11 (2018)
Mardani M, Gong E, Cheng JY, Vasanawala S, Zaharchuk G, Alley M, Thakur N, Han S, Dally W, Pauly JM et al (2017) Deep generative adversarial networks for compressed sensing automates MRI. arXiv:1706.00051
Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D (2018) A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging 37:491–503
Parisot S, Ktena SI, Ferrante E, Lee M, Moreno RG, Glocker B, Rueckert D (2017) Spectral graph convolutions for population-based disease prediction. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 177–185
http://fcon_1000.projects.nitrc.org/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Pandey, P., Pallavi, S., Pandey, S.C. (2020). Pragmatic Medical Image Analysis and Deep Learning: An Emerging Trend. In: Verma, O., Roy, S., Pandey, S., Mittal, M. (eds) Advancement of Machine Intelligence in Interactive Medical Image Analysis. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1100-4_1
Download citation
DOI: https://doi.org/10.1007/978-981-15-1100-4_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1099-1
Online ISBN: 978-981-15-1100-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)