Abstract
A fully automatic segmentation method for ultrasound images of the common carotid artery is proposed in this paper. The goal of this procedure is the detection of the arterial wall layers to assist in the evaluation of the Intima-Media Thickness (IMT), which is an early indicator of atherosclerosis and, therefore, of the cardiovascular risk. By measuring and monitoring the IMT, specialists are able to detect the incipient thickening of the arteries when the patient is still asymptomatic and to prescribe the appropriate preventive care. The proposed methodology is completely based on Machine Learning and it applies Auto-Encoders and Deep Learning to obtain abstract and efficient data representations. A set of 45 ultrasound images have been used in the validation of the suggested system. In particular, the resulting automatic contours for each image have been compared with four manual segmentations performed by two different observers. This study demonstrates the accuracy of our segmentation method, which achieves the correct recognition of the arterial layers in all the tested images in a totally user-independent and repeatable manner.
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Y. Bengio, A. Courville, P. Vincent, Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
L. Deng, D. Yu, Deep learning: methods and applications. Technical Report MSR-TR-2014-21 (January 2014), http://research.microsoft.com/apps/pubs/default.aspx?id=209355
G.B. Huang, H. Zhou, X. Ding, R. Zhang, Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B-Cybern. 42(2), 513–529 (2012)
G.B. Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
L.L.C. Kasun, H. Zhou, G.B. Huang, C.M. Vong, Representational learning with extreme learning machine for big data. IEEE Intell. Syst. 28(6), 31–34 (2013)
R.M. Menchón-Lara, M.C. Bastida-Jumilla, J. Morales-Sánchez, J.L. Sancho-Gómez, Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks. Med. Biol. Eng. Comput. 52(2), 169–181 (2014)
R.M. Menchón-Lara, J.L. Sancho-Gómez, Fully automatic segmentation of ultrasound common carotid artery images based on machine learning. Neurocomputing 151(Part 1(0)), 161–167 (2015)
K.S. Nikita, Atherosclerosis: the evolving role of vascular image analysis. Comput. Med. Imaging Graph. 37(1), 1–3 (2013)
P.J. Touboul et al., Mannheim carotid intima-media thickness and plaque consensus (2004–2006-2011). Cerebrovasc. Dis. 34, 290–296 (2012)
WHO, Global atlas on cardiovascular disease prevention and control. Online, www.who.int/cardiovascular_diseases/en/
Acknowledgments
Authors would like to thank the Radiology Department of ‘Hospital Universitario Virgen de la Arrixaca’ (Murcia, Spain) for their kind collaboration and for providing all the ultrasound images used.
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Menchón-Lara, RM. et al. (2015). Using Machine Learning Techniques for the Automatic Detection of Arterial Wall Layers in Carotid Ultrasounds. In: Mohamed, A., Novais, P., Pereira, A., Villarrubia González, G., Fernández-Caballero, A. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent Systems and Computing, vol 376. Springer, Cham. https://doi.org/10.1007/978-3-319-19695-4_20
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DOI: https://doi.org/10.1007/978-3-319-19695-4_20
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