Abstract
Deep Learning techniques have recently been widely used for medical image analysis, which has shown encouraging results especially for large datasets. In particular, convolutional neural network has shown better capabilities to segment and/or classify medical images like ultrasound and CT scan images in comparison to previously used conventional machine learning techniques. This chapter includes applications of deep learning techniques in two different image modalities used in medical image analysis domain. The application of convolutional neural network in medical images is shown using ultrasound images to segment a collection of nerves known as Brachial Plexus. Deep learning technique is also applied to classify different stages of diabetic retinopathy using color fundus retinal photography.
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
References
Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
S.C.B. Lo, H.P. Chan, J.S. Lin, H. Li, M.T. Freedman, S.K. Mun, Artificial convolution neural network for medical image pattern recognition. Neural Netw. 8(7), 1201–1214 (1995)
A.I. Krizhevsky, S.G. Hinton, Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1106–1114 (2012)
M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, S. Mougiakakou, Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016)
T. Liu, S. Xie, J. Yu, L. Niu, W. Sun, Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, 2017 (2017), pp. 919–923
A. Rajkomar, S. Lingam, A.G. Taylor, High-throughput classification of radiographs using deep convolutional neural networks. J. Digit. Imaging 30(1), 95–101 (2017)
S. Pereira, A. Pinto, V. Alves, C.A. Silva, Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)
H.R. Roth, A. Farag, L. Lu, E.B. Turkbey, R.M. Summers, Deep convolutional networks for pancreas segmentation in CT imaging. Proc. SPIE Medical Imaging pp. 94–131 (2015)
D. Ciresan, A. Giusti, L.M. Gambardella, J. Schmidhuber, Deep neural networks segment neuronal membranes in electron microscopy images, in Advances in Neural Information Processing Systems, vol. 25 (Red Hook, NY, 2012), pp. 2843–2851
D.A. Pollen, S.F. Ronner, Visual cortical neurons as localized spatial frequency filters. IEEE Trans. Syst. Man Cybern. 13(5), 907–916 (1983)
I. Pitas, A.N. Venetsanopoulos, Edge detectors based on nonlinear filters. IEEE Trans. Pattern Anal. Mach. Intell. 4, 538–550 (1986)
D. Scherer, A. Müller, S. Behnke, Evaluation of pooling operations in convolutional architectures for object recognition, in Artificial Neural Networks—ICANN 2010. Lecture Notes in Computer Science, ed. by K. Diamantaras, W. Duch, L.S. Iliadis, vol. 6354 (Springer, Berlin, Heidelberg, 2010)
H. Guo, S.B. Gelfand, Analysis of gradient descent learning algorithms for multilayer feedforward neural networks. IEEE Trans. Circuits Syst. 38(8), 883–894 (1991)
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, Caffe: convolutional architecture for fast feature embedding. arXiv:1408.5093 (2014)
M. Li, T. Zhang, Y. Chen, A. Smola, Efficient mini-batch training for stochastic optimization, in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2014)
A. Von Lehmen, E.G. Paek, P.F. Liao, A. Marrakchi, J.S. Patel, Factors influencing learning by backpropagation, in IEEE International Conference on Neural Networks, San Diego, CA, USA, vol. 1 (1988), pp. 335–341
http://www.assh.org/handcare/hand-arm-injuries/Brachial-Plexus-Injury#prettyPhoto
K. Saladin, Anatomy and Physiology, 7 edn. (McGraw Hill, New York 2015), pp. 489–491
F. Lapegue, M. Faruch-Bilfeld, X. Demondion, C. Apredoaei, M.A. Bayol, H. Artico, H. Chiavassa-Gandois, J.J. Railhac, N. Sans, Ultrasonography of the brachial plexus, normal appearance and practical applications. Diagn. Interv. Imaging 95(3), 259–275 (2014)
A. Perlas, V.W.S. Chan, M. Simons, Brachial plexus examination and localization using ultrasound and electrical stimulation: a volunteer study. Anesthes 99(2), 429–435 (2003)
Ultrasound Nerve Segmentation Challenge, https://www.kaggle.com/c/ultrasound-nerve-segmentation/data (2016)
O. Ronneberger, P. Fischer, T. Brox, U-Net: convolutional networks for biomedical image segmentation. arXiv:1505.04597 [cs] (2015)
Cell Tracking Challenge, http://www.codesolorzano.com/Challenges/CTC/Welcome.html (2017)
H. Ide, T. Kurita, Improvement of learning for CNN with ReLU activation by sparse regularization, in International Joint Conference on Neural Networks, Anchorage, AK, USA (2017), pp. 2684–2691
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas (2016), pp. 2818–2826
A.A. Taha, A. Hanbury, Metrics for evaluating 3D medical image segmentation: analysis selection and tool. BMC Med. Imaging 15(1), 15–29 (2015)
R. Williams, M. Airey, H. Baxter, J. Forrester, T. Kennedy-Martin, A. Girach, Epidemiology of diabetic retinopathy and macular oedema: a systematic review. Eye 18(10), 963–983 (2004)
J. Cornwall, S.A. Kaveeshwar, The current state of diabetes mellitus in India. Australas. Med. J. 45–48 (2014)
Diabetic Retinopathy Detection Challenge, https://www.kaggle.com/c/diabetic-retinopathy-detection (2015)
K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. arXiv:1502.01852 (2015)
N. Srivastava, G.E. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
P. Baldi, P.J. Sadowski, Understanding dropout, in Advances in Neural Information Processing Systems, ed. by C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, K.Q. Weinberger, vol. 26 (2013), pp. 2814–2822
C. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer, New York, 2006)
Acknowledgements
The authors would like to thank Kaggle for making the ultrasound nerve segmentation and diabetic retinopathy detection datasets publicly available. Thanks to California Healthcare Foundation for sponsoring the diabetic retinopathy detection competition and EyePacs for providing the retinal images.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Maitra, S., Ghosh, R., Ghosh, K. (2019). Applications of Deep Learning in Medical Imaging. In: Balas, V., Roy, S., Sharma, D., Samui, P. (eds) Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-030-11479-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-11479-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11478-7
Online ISBN: 978-3-030-11479-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)