Advertisement

Ultrasound-Based Detection of Lung Abnormalities Using Single Shot Detection Convolutional Neural Networks

  • Sourabh Kulhare
  • Xinliang Zheng
  • Courosh Mehanian
  • Cynthia Gregory
  • Meihua Zhu
  • Kenton Gregory
  • Hua Xie
  • James McAndrew Jones
  • Benjamin Wilson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11042)

Abstract

Ultrasound imaging can be used to identify a variety of lung pathologies, including pneumonia, pneumothorax, pleural effusion, and acute respiratory distress syndrome (ARDS). Ultrasound lung images of sufficient quality are relatively easy to acquire, but can be difficult to interpret as the relevant features are mostly non-structural and require expert interpretation. In this work, we developed a convolutional neural network (CNN) algorithm to identify five key lung features linked to pathological lung conditions: B-lines, merged B-lines, lack of lung sliding, consolidation and pleural effusion. The algorithm was trained using short ultrasound videos of in vivo swine models with carefully controlled lung conditions. Key lung features were annotated by expert radiologists and snonographers. Pneumothorax (absence of lung sliding) was detected with an Inception V3 CNN using simulated M-mode images. A single shot detection (SSD) framework was used to detect the remaining features. Our results indicate that deep learning algorithms can successfully detect lung abnormalities in ultrasound imagery. Computer-assisted ultrasound interpretation can place expert-level diagnostic accuracy in the hands of low-resource health care providers.

Keywords

Lung ultrasound Deep learning Convolutional neural networks 

References

  1. 1.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  2. 2.
    Testa, A., Soldati, G., Copetti, R., Giannuzzi, R., Portale, G., Gentiloni-Silveri, N.: Early recognition of the 2009 pandemic influenza A (H1N1) pneumonia by chest ultrasound. Crit. Care 16(1), R30 (2011)CrossRefGoogle Scholar
  3. 3.
    Parlamento, S., Copetti, R., Bartolomeo, S.D.: Evaluation of lung ultrasound for the diagnosis of pneumonia in the ED. Am. J. Emerg. 27(4), 379–384 (2009)CrossRefGoogle Scholar
  4. 4.
    Weitzel, W., Hamilton, J., Wang, X., Bull, J., Vollmer, A.: Quantitative lung ultrasound comet measurement: method and initial clinical results. Blood Purif. 39, 37–44 (2015)CrossRefGoogle Scholar
  5. 5.
    Anantrasirichai, N., Allinovi, M., Hayes, W., Achim, A.: Automatic B-line detection in paediatric lung ultrasound. In: 2016 IEEE International Ultrasonics Symposium (IUS), Tours, France (2016)Google Scholar
  6. 6.
    Moshavegh, R., et al.: Novel automatic detection of pleura and B-lines (comet-tail artifacts) on in vivo lung ultrasound scans. In: SPIE Medical Imaging 2016 (2016)Google Scholar
  7. 7.
    Fang, S., Wang, Y.R.B.: Automatic detection and evaluation of B-lines by lung ultrasound. NYU, New York CityGoogle Scholar
  8. 8.
    Huang, Q., Zhang, F., Li, X.: Machine learning in ultrasound computer-aided diagnostic systems: a survey. BioMed Res. Int. (2018)Google Scholar
  9. 9.
    Jabbar, S., Day, C., Heinz, N., Chadwick, E.: Using Convolutional Neural Network for edge detection in musculoskeletal ultrasound images. In: International Joint Conference on Neural Networks, pp. 4619–4626 (2016)Google Scholar
  10. 10.
    Shin, J., Tajbakhsh, N., Hurst, R., Kendall, C., Liang, J.: Automating carotid intima-media thickness video interpretation with convolutional neural networks. In: Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 2526–2535 (2016)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Liu, W., et al.: SSD: Single Shot MultiBox Detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_2CrossRefGoogle Scholar
  13. 13.
    Volpicelli, G., et al.: International evidence-based recommendations for point-of-care lung ultrasound. Intensive Care Med. 38(4), 577–591 (2012)CrossRefGoogle Scholar
  14. 14.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580–587 (2014)Google Scholar
  15. 15.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)Google Scholar
  16. 16.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 91–99 (2015)Google Scholar
  17. 17.
    Huang, J., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: The Conference on Computer Vision and Pattern Recognition (2017)Google Scholar
  18. 18.
    Szegedy, C., Vanhoucke, V., Loffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: The Conference on Computer Vision and Pattern Recognition (2015)Google Scholar
  19. 19.
    Jia, D., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: The Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  20. 20.
    Omar, Z., et al.: An explorative childhood pneumonia analysis based on ultrasonic imaging texture features. In: 11th International Symposium on Medical Information Processing and Analysis, vol. 9681 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sourabh Kulhare
    • 1
  • Xinliang Zheng
    • 1
  • Courosh Mehanian
    • 1
  • Cynthia Gregory
    • 2
  • Meihua Zhu
    • 2
  • Kenton Gregory
    • 1
    • 2
  • Hua Xie
    • 2
  • James McAndrew Jones
    • 2
  • Benjamin Wilson
    • 1
  1. 1.Intellectual Ventures LaboratoryBellevueUSA
  2. 2.Oregon Health Sciences UniversityPortlandUSA

Personalised recommendations