Skip to main content
SpringerLink
Log in

Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks

  • Conference paper
  • First Online:
Deep Learning and Data Labeling for Medical Applications (DLMIA 2016, LABELS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10008))

Abstract

Deep convolutional neural networks have achieved great results on image classification problems. In this paper, a new method using a deep convolutional neural network for detecting blood vessels in B-mode ultrasound images is presented. Automatic blood vessel detection may be useful in medical applications such as deep venous thrombosis detection, anesthesia guidance and catheter placement. The proposed method is able to determine the position and size of the vessels in images in real-time. 12,804 subimages of the femoral region from 15 subjects were manually labeled. Leave-one-subject-out cross validation was used giving an average accuracy of 94.5 %, a major improvement from previous methods which had an accuracy of 84 % on the same dataset. The method was also validated on a dataset of the carotid artery to show that the method can generalize to blood vessels on other regions of the body. The accuracy on this dataset was 96 %.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abolmaesumi, P., Sirouspour, M., Salcudean, S.: Real-time extraction of carotid artery contours from ultrasound images. In: Proceedings 13th IEEE Symposium on Computer-Based Medical Systems, CBMS 2000, pp. 181–186. IEEE Computer Society (2000)

    Google Scholar 

  2. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. Adv. Neural Inf. Process. Syst. 19(1), 153–160 (2007)

    Google Scholar 

  3. Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448. IEEE, December 2015

    Google Scholar 

  4. Glorot, X., Bordes, A., Bengio, Y.: Deep Sparse rectifier neural networks. In: 14th International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)

    Google Scholar 

  5. Guerrero, J., Salcudean, S.E., McEwen, J.A., Masri, B.A., Nicolaou, S.: System for deep venous thombosis detection using objective compression measures. IEEE Trans. Biomed. Eng. 53(5), 845–854 (2006)

    Article  Google Scholar 

  6. Guerrero, J., Salcudean, S.E., McEwen, J.A., Masri, B.A., Nicolaou, S.: Real-time vessel segmentation and tracking for ultrasound imaging applications. IEEE Trans. Med. Imaging 26(8), 1079–1090 (2007)

    Article  Google Scholar 

  7. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, pp. 675–678 (2014)

    Google Scholar 

  8. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  9. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)

    Google Scholar 

  10. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, pp. 91–99, June 2015

    Google Scholar 

  11. Smistad, E., Bozorgi, M., Lindseth, F.: FAST: framework for heterogeneous medical image computing and visualization. Int. J. Comput. Assist. Radiol. Surg. 10(11), 1811–1822 (2015)

    Article  Google Scholar 

  12. Smistad, E., Lindseth, F.: Real-time automatic artery segmentation, reconstruction and registration for ultrasound-guided regional anaesthesia of the femoral nerve. IEEE Trans. Med. Imaging 35(3), 752–761 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Erik Smistad & Lasse Løvstakken

  2. SINTEF Medical Technology, Trondheim, Norway

    Erik Smistad

Authors
  1. Erik Smistad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lasse Løvstakken
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erik Smistad .

Editor information

Editors and Affiliations

  1. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  2. Technical University of Munich, Garching, Germany

    Diana Mateus

  3. Technical University of Munich, Garching, Germany

    Loïc Peter

  4. University of Queensland, St Lucia, Queensland, Australia

    Andrew Bradley

  5. Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  6. University of Oxford, Oxford, United Kingdom

    Vasileios Belagiannis

  7. Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  8. Instituto Superior Técnico, Lisbon, Portugal

    Jacinto C. Nascimento

  9. Delft University of Technology, Delft, The Netherlands

    Marco Loog

  10. University of South Australia, Adelaide, South Australia, Australia

    Zhi Lu

  11. Universidade do Porto, Porto, Portugal

    Jaime S. Cardoso

  12. Google DeepMind, London, United Kingdom

    Julien Cornebise

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Smistad, E., Løvstakken, L. (2016). Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks. In: Carneiro, G., et al. Deep Learning and Data Labeling for Medical Applications. DLMIA LABELS 2016 2016. Lecture Notes in Computer Science(), vol 10008. Springer, Cham. https://doi.org/10.1007/978-3-319-46976-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46976-8_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46975-1

  • Online ISBN: 978-3-319-46976-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation