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.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Aboofazeli, M., Abolmaesumi, P., Mousavi, P., Fichtinger, G.: A new scheme for curved needle segmentation in three-dimensional ultrasound images. In: IEEE Int. Symp. Biomedical Imaging: Nano to Macro, pp. 1067–1070 (2009)
Adebar, T.K., Okamura, A.M.: 3D segmentation of curved needles using doppler ultrasound and vibration. In: Barratt, D., Cotin, S., Fichtinger, G., Jannin, P., Navab, N. (eds.) IPCAI 2013. LNCS, vol. 7915, pp. 61–70. Springer, Heidelberg (2013)
Cheung, S., Rohling, R.: Enhancement of needle visibility in ultrasound-guided percutaneous procedures. Ultrasound Med. Biol. 30(5), 617–624 (2004)
Ding, M., Cardinal, H.N., Fenster, A.: Automatic needle segmentation in 3D ultrasound images using two orthogonal 2D image projections. Med. Phys. 30(2), 222–234 (2003)
Ding, M., Fenster, A.: A real-time biopsy needle segmentation technique using hough transform. Med. Phys. 30(8), 2222–2233 (2003)
Fronheiser, M.P., Idriss, S.F., Wolf, P.D., Smith, S.W.: Vibrating interventional device detection using real-time 3-D color doppler. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 55(6), 1355–1362 (2008)
Harmat, A., Rohling, R.N., Salcudean, S.E.: Needle tip localization using stylet vibration. Ultrasound Med. Biol. 32(9), 1339–1348 (2006)
Holen, J., Waag, R.C., Gramiak, R.: Improved needle-tip visualization by color Doppler sonography. Am. J. Roentgenol. 156, 401–402 (1985)
Klein, S.M., Fronheiser, M.P., Reach, J., Nielsen, K.C., Smith, S.W.: Piezoelectric vibrating needle and catheter for enhancing ultrasound-guided peripheral nerve blocks. Anesth. Analg. 105(6), 1858–1860 (2007)
Mung, J., Vignon, F., Jain, A.: A non-disruptive technology for robust 3D tool tracking for ultrasound-guided interventions. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 153–160. Springer, Heidelberg (2011)
Neshat, H.R.S., Patel, R.V.: Real-time parametric curved needle segmentation in 3D ultrasound images. In: IEEE RAS EMBS Int. Conf. Biomedical Robotics Biomechatronics, pp. 670–675 (2008)
Okazawa, S.H., Ebrahimi, R., Chuang, J., Rohling, R.N., Salcudean, S.E.: Methods for segmenting curved needles in ultrasound images. Med. Im. Anal. 10(3), 330–342 (2006)
Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11, 23–27 (1975)
Uhercík, M., Kybic, J., Liebgott, H., Cachard, C.: Model fitting using RANSAC for surgical tool localization in 3D ultrasound images. IEEE Trans. Biomed. Eng. 57(8), 1907–1916 (2010)
Zhou, H., Qiu, W., Ding, M., Zhang, S.: Automatic needle segmentation in 3d ultrasound images using 3d improved hough transform. In: Miga, M., Cleary, K. (eds.) SPIE Proceedings 2008, vol. 6918, pp. 691821–691821-9 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)