Recent Advances in Point-of-Care Ultrasound Using the \({\textit{ImFusion Suite}}\) for Real-Time Image Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11042)


Medical ultrasound is rapidly advancing both through more powerful hardware and software; in combination these allow the modality to become an ever more indispensable point-of-care tool. In this paper, we summarize some recent developments on the image analysis side that are enabled through the proprietary ImFusion Suite software and corresponding software development kit (SDK). These include 3D reconstruction of arbitrary untracked 2D US clips, image filtering and classification, speed-of-sound calibration and live acquisition parameter tuning in a visual servoing fashion.


Real-time Image Analysis Point-of-care Ultrasound (POCUS) Computer Assisted Orthopaedic Surgery (CAOS) Acquisition Pipeline Three-dimensional Ultrasound Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially supported by H2020-FTI grant (number 760380) delivered by the European Union.


  1. 1.
    Campbell, S.J., Bechara, R., Islam, S.: Point-of-care ultrasound in the intensive care unit. Clin. Chest Med. 39(1), 79–97 (2018)CrossRefGoogle Scholar
  2. 2.
    Che, C., Mathai, T.S., Galeotti, J.: Ultrasound registration: a review. Methods 115, 128–143 (2017)CrossRefGoogle Scholar
  3. 3.
    Mozaffari, M.H., Lee, W.S.: Freehand 3-D ultrasound imaging: a systematic review. Ultrasound Med. Biol. 43(10), 2099–2124 (2017)CrossRefGoogle Scholar
  4. 4.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  5. 5.
    Reinertsen, I., Iversen, D., Lindseth, F., Wein, W., Unsgård, G.: Intra-operative ultrasound based correction of brain-shift. In: Intraoperative Imaging Society Conference, Hanover, Germany (2017)Google Scholar
  6. 6.
    Kikinis, R., Pieper, S.D., Vosburgh, K.G.: 3D slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz, F. (ed.) Intraoperative Imaging and Image-Guided Therapy, pp. 277–289. Springer, New York (2014). Scholar
  7. 7.
    Ungi, T., Lasso, A., Fichtinger, G.: Open-source platforms for navigated image-guided interventions. Med. Image Anal. 33, 181–186 (2016)CrossRefGoogle Scholar
  8. 8.
    Lasso, A., Heffter, T., Rankin, A., Pinter, C., Ungi, T., Fichtinger, G.: PLUS: open-source toolkit for ultrasound-guided intervention systems. IEEE Trans. Biomed. Eng. 61(10), 2527–2537 (2014)CrossRefGoogle Scholar
  9. 9.
    Askeland, C., et al.: CustusX: an open-source research platform for image-guided therapy. IJCARS 11(4), 505–519 (2015)CrossRefGoogle Scholar
  10. 10.
    Göbl, R., Navab, N., Hennersperger, C.: SUPRA: open source software defined ultrasound processing for real-time applications. Int. J. Comput. Assist. Radiol. Surg. 13(6), 759–767 (2017)CrossRefGoogle Scholar
  11. 11.
    Zettinig, O., et al.: 3D ultrasound registration-based visual servoing for neurosurgical navigation. IJCARS 12(9), 1607–1619 (2017)CrossRefGoogle Scholar
  12. 12.
    Riva, M., et al.: 3D intra-operative ultrasound and MR image guidance: pursuing an ultrasound-based management of brainshift to enhance neuronavigation. IJCARS 12(10), 1711–1725 (2017)CrossRefGoogle Scholar
  13. 13.
    Nagaraj, Y., Benedicks, C., Matthies, P., Friebe, M.: Advanced inside-out tracking approach for real-time combination of MRI and US images in the radio-frequency shielded room using combination markers. In: EMBC, pp. 2558–2561. IEEE (2016)Google Scholar
  14. 14.
    Şen, H.T., et al.: Cooperative control with ultrasound guidance for radiation therapy. Front. Robot. AI 3, 49 (2016)Google Scholar
  15. 15.
    Wein, W., Khamene, A.: Image-based method for in-vivo freehand ultrasound calibration. In: SPIE Medical Imaging 2008, San Diego, February 2008Google Scholar
  16. 16.
    Karamalis, A., Wein, W., Kutter, O., Navab, N.: Fast hybrid freehand ultrasound volume reconstruction. In: Miga, M., Wong, I., Kenneth, H. (eds.) Proceedings of the SPIE, vol. 7261, pp. 726114–726118 (2009)Google Scholar
  17. 17.
    Prevost, R., et al.: 3D freehand ultrasound without external tracking using deep learning. Med. Image Anal. 48, 187–202 (2018)CrossRefGoogle Scholar
  18. 18.
    Prager, R.W., Gee, A.H., Treece, G.M., Cash, C.J., Berman, L.H.: Sensorless freehand 3-D ultrasound using regression of the echo intensity. Ultrasound Med. Biol. 29(3), 437–446 (2003)CrossRefGoogle Scholar
  19. 19.
    Gao, H., Huang, Q., Xu, X., Li, X.: Wireless and sensorless 3D ultrasound imaging. Neurocomputing 195(C), 159–171 (2016)CrossRefGoogle Scholar
  20. 20.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  21. 21.
    Salehi, M., Prevost, R., Moctezuma, J.-L., Navab, N., Wein, W.: Precise ultrasound bone registration with learning-based segmentation and speed of sound calibration. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 682–690. Springer, Cham (2017). Scholar
  22. 22.
    El-Zehiry, N., Yan, M., Good, S., Fang, T., Zhou, S.K., Grady, L.: Learning the manifold of quality ultrasound acquisition. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 122–130. Springer, Heidelberg (2013). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.ImFusion GmbHMunichGermany
  2. 2.Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany

Personalised recommendations