Deep Learning for Sensorless 3D Freehand Ultrasound Imaging

  • Raphael PrevostEmail author
  • Mehrdad Salehi
  • Julian Sprung
  • Alexander Ladikos
  • Robert Bauer
  • Wolfgang Wein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)


3D freehand ultrasound imaging is a very promising imaging modality but its acquisition is often neither portable nor practical because of the required external tracking hardware. Building a sensorless solution that is fully based on image analysis would thus have many potential applications. However, previously proposed approaches rely on physical models whose assumptions only hold on synthetic or phantom datasets, failing to translate to actual clinical acquisitions with sufficient accuracy. In this paper, we investigate the alternative approach of using statistical learning to circumvent this problem. To that end, we are leveraging the unique modeling capabilities of convolutional neural networks in order to build an end-to-end system where we directly predict the ultrasound probe motion from the images themselves. Based on thorough experiments using both phantom acquisitions and a set of 100 in-vivo long ultrasound sweeps for vein mapping, we show that our novel approach significantly outperforms the standard method and has direct clinical applicability, with an average drift error of merely 7\(\%\) over the whole length of each ultrasound clip.

Supplementary material

451304_1_En_71_MOESM1_ESM.pdf (3 mb)
Supplementary material 1 (pdf 3066 KB)


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Raphael Prevost
    • 1
    Email author
  • Mehrdad Salehi
    • 1
    • 2
  • Julian Sprung
    • 3
  • Alexander Ladikos
    • 1
  • Robert Bauer
    • 3
  • Wolfgang Wein
    • 1
  1. 1.ImFusion GmbHMunichGermany
  2. 2.Computer Aided Medical Procedures (CAMP)TU MunichMunichGermany
  3. 3.Piur Imaging GmbHViennaAustria

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