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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2014: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 pp 381–388Cite as

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Real-Time 3D Curved Needle Segmentation Using Combined B-Mode and Power Doppler Ultrasound

Real-Time 3D Curved Needle Segmentation Using Combined B-Mode and Power Doppler Ultrasound

  • Joseph D. Greer20,
  • Troy K. Adebar20,
  • Gloria L. Hwang20 &
  • …
  • Allison M. Okamura20 
  • Conference paper
  • 5139 Accesses

  • 12 Citations

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8674))

Abstract

This paper presents a real-time segmentation method for curved needles in biological tissue based on analysis of B-mode and power Doppler images from a tracked 2D ultrasound transducer. Mechanical vibration induced by an external voice coil results in a Doppler response along the needle shaft, which is centered around the needle section in the ultrasound image. First, B-mode image analysis is performed within regions of interest indicated by the Doppler response to create a segmentation of the needle section in the ultrasound image. Next, each needle section is decomposed into a sequence of points and transformed into a global coordinate system using the tracked transducer pose. Finally, the 3D shape is reconstructed from these points. The results of this method differ from manual segmentation by 0.71±0.55 mm in needle tip location and 0.38±0.27 mm along the needle shaft. This method is also fast, taking 5-10 ms to run on a standard PC, and is particularly advantageous in robotic needle steering, which involves thin, curved needles with poor echogenicity.

Keywords

  • Ultrasound Image
  • Manual Segmentation
  • Power Doppler Ultrasound
  • Needle Shape
  • Power Doppler Image

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.

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References

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Author information

Authors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Joseph D. Greer, Troy K. Adebar, Gloria L. Hwang & Allison M. Okamura

Authors
  1. Joseph D. Greer
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  2. Troy K. Adebar
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  3. Gloria L. Hwang
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  4. Allison M. Okamura
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Editor information

Editors and Affiliations

  1. MIT CSAIL, 32 Vassar Street, 02139, Cambridge, MA, USA

    Polina Golland

  2. Department of Radiology, Brigham and Women’s Hospital, 75 Francis St., 02115, Boston, MA, USA

    Nobuhiko Hata

  3. IRISA, CNRS/Inria Research Unit Visages, Campus Universitaire de Beaulieu, 35042, Rennes Cedex, France,

    Christian Barillot

  4. Pattern Recognition Lab, University Erlangen-Nuremberg, Martensstr. 3, 91058, Erlangen, Germany

    Joachim Hornegger

  5. Harvard School of Engineering and Applied Sciences, 323 Pierce Hall, 29 Oxford Street, 02138, Cambridge, MA, USA

    Robert Howe

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© 2014 Springer International Publishing Switzerland

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Greer, J.D., Adebar, T.K., Hwang, G.L., Okamura, A.M. (2014). Real-Time 3D Curved Needle Segmentation Using Combined B-Mode and Power Doppler Ultrasound. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8674. Springer, Cham. https://doi.org/10.1007/978-3-319-10470-6_48

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  • DOI: https://doi.org/10.1007/978-3-319-10470-6_48

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10469-0

  • Online ISBN: 978-3-319-10470-6

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