Abstract
This paper presents an ultrasound (US) volume reconstruction method only from US image sequences using deep learning. The proposed method employs the convolutional neural network (CNN) to estimate the position of a 2D US probe only from US images. Our CNN model consists of two networks: feature extraction and motion estimation. We also introduce the consistency loss function to enforce. Through a set of experiments using US image sequence datasets with ground-truth motion measured by a motion capture system, we demonstrate that the proposed method exhibits the efficient performance on probe localization and volume reconstruction compared with the conventional method.
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References
Balakrishnan, S., Patel, R., Illanes, A., Friebe, M.: Novel similarity metric for image-based out-of-plane motion estimation in 3D ultrasound. In: Proceedings of the International Conference on IEEE Engineering in Medicine and Biology Society, pp. 5739–5742, July 2019
Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks In: Proceedings of the International Conference on Computer Vision, pp. 2758–2766, December 2015
Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45103-X_50
Gee, A., Prager, R., Treece, G., Berman, L.: Engineering a freehand 3D ultrasound system. Pattern Recognit. Lett. 24(4–5), 757–777 (2003)
Godard, C., Aodha, O.M., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. 270–279, July 2017
Goldsmith, A., Pedersen, P., Szabo, T.: An inertial-optical tracking system for portable, quantitative, 3D ultrasound. In: Proceedings of the IEEE International Ultrasonics Symposium, pp. 45–49, November 2008
Hastenteufel, M., Vetter, M., Meinzer, H.P., Wolf, I.: Effect of 3D ultrasound probes on the accuracy of electromagnetic tracking systems. Ultrasound Med. Biol. 32(9), 1359–1368 (2006)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 770–778, June 2016
Horvath, S., et al.: Towards an ultrasound probe with vision: structured light to determine surface orientation. In: Linte, C.A., Moore, J.T., Chen, E.C.S., Holmes, D.R. (eds.) AE-CAI 2011. LNCS, vol. 7264, pp. 58–64. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32630-1_6
Ito, K., Yodokawa, K., Aoki, T., Ohmiya, J., Kondo, S.: A probe-camera system for 3D ultrasound image reconstruction. In: Cardoso, M.J., et al. (eds.) BIVPCS/POCUS -2017. LNCS, vol. 10549, pp. 129–137. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67552-7_16
Ito, S., Ito, K., Aoki, T., Ohmiya, J., Kondo, S.: Probe localization using structure from motion for 3D ultrasound image reconstruction. In: Proceedings of the International Symposium on Biomedical Imaging, pp. 68–71, April 2017
Lange, T., Kraft, S., Eulenstein, S., Lamecker, H., Schlag, P.: Automatic calibration of 3D ultrasound probes. In: Proceedings of the Bildverarbeitung für die Medizin, pp. 169–173, March 2011
Nelson, T.R., Pretorius, D.H.: Three-dimensional ultrasound imaging. Ultrasound Med. Biol. 24(9), 1243–1270 (1998)
Prevost, R.R., et al.: 3D freehand ultrasound without external tracking using deep learning. Med. Image Anal. 48, 187–202 (2018)
Prevost, R., Salehi, M., Sprung, J., Ladikos, A., Bauer, R., Wein, W.: Deep learning for sensorless 3D freehand ultrasound imaging. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 628–636. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_71
Rafii-Tari, H., Abolmaesumi, P., Rohling, R.: Panorama ultrasound for guiding epidural anesthesia: a feasibility study. In: Taylor, R.H., Yang, G.-Z. (eds.) IPCAI 2011. LNCS, vol. 6689, pp. 179–189. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21504-9_17
Rousseau, F., Hellier, P., Barillot, C.: A fully automatic calibration procedure for freehand 3D ultrasound. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 985–988, July 2002
Stolka, P., Kang, H., Choti, M., Boctor, E.: Multi-DoF probe trajectory reconstruction with local sensors for 2D-to-3D ultrasound. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 316–319, April 2010
Sun, S.-Y., Gilbertson, M., Anthony, B.W.: Probe localization for freehand 3D ultrasound by tracking skin features. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 365–372. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10470-6_46
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Miura, K., Ito, K., Aoki, T., Ohmiya, J., Kondo, S. (2020). Localizing 2D Ultrasound Probe from Ultrasound Image Sequences Using Deep Learning for Volume Reconstruction. In: Hu, Y., et al. Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis. ASMUS PIPPI 2020 2020. Lecture Notes in Computer Science(), vol 12437. Springer, Cham. https://doi.org/10.1007/978-3-030-60334-2_10
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