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Probabilistic Edge Map (PEM) for 3D Ultrasound Image Registration and Multi-atlas Left Ventricle Segmentation

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Functional Imaging and Modeling of the Heart (FIMH 2015)

Abstract

Automated left ventricle (LV) segmentation in 3D ultrasound (3D-US) remains a challenging research problem due to variable image quality and limited field-of-view. Modern segmentation approaches (shape, appearance and contour model based surface fitting) require an accurate initialization and good image boundary features to obtain reliable and consistent results. They are therefore not well suited for this problem. The proposed method overcomes those limitations with a novel and generic 3D-US image boundary representation technique: Probabilistic Edge Map (PEM). This new representation captures regularized and complete edge responses from standard 3D-US images. PEM is utilized in a multi-atlas LV segmentation framework to spatially align target and atlas images. Experiments on data from the MICCAI CETUS challenge show that the proposed approach is better suited for LV segmentation than the active contour, appearance and voxel classification approaches, achieving lower surface distance errors and better LV volume estimates.

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Notes

  1. 1.

    Locally weighted and majority voting fusion methods were also evaluated in the experiments, and the best results were obtained with the global fusion method.

  2. 2.

    https://miccai.creatis.insa-lyon.fr/miccai/community/1.

References

  1. Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D.: Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. NeuroImage 46(3), 726–738 (2009)

    Article  Google Scholar 

  2. Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de Solórzano, C.: Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans. Med. Imag. 28, 1266–1277 (2009)

    Article  Google Scholar 

  3. Barbosa, D., Friboulet, D., D’hooge, J., Bernard, O.: Fast tracking of the left ventricle using global anatomical affine optical flow and local recursive block matching. In: Proceedings of MICCAI CETUS Challenge (2014)

    Google Scholar 

  4. Barbosa, D., et al.: Fast and fully automatic 3-D echocardiographic segmentation using B-spline explicit active surfaces: feasibility study and validation in a clinical setting. Ultrasound Med. Biol. 39(1), 89–101 (2013)

    Article  Google Scholar 

  5. Cachier, P., Pennec, X.: 3D non-rigid registration by gradient descent on a Gaussian windowed similarity measure using convolutions. In: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 182–189 (2000)

    Google Scholar 

  6. Dollár, P., Zitnick, C.L.: Structured forests for fast edge detection. In: ICCV, pp. 1841–1848. IEEE (2013)

    Google Scholar 

  7. Domingos, J.S., Stebbing, R.V., Leeson, P., Noble, J.A.: Structured random forests for myocardium delineation in 3D echocardiography. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 215–222. Springer, Heidelberg (2014)

    Google Scholar 

  8. Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)

    Article  MathSciNet  Google Scholar 

  9. Lempitsky, V., Verhoek, M., Noble, J.A., Blake, A.: Random forest classification for automatic delineation of myocardium in real-time 3D echocardiography. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 447–456. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  10. Oktay, O., Shi, W., Caballero, J., Keraudren, K., Rueckert, D.: Sparsity based spectral embedding: application to multi-atlas echocardiography segmentation. In: Proceedings of MICCAI STMI Workshop (2014)

    Google Scholar 

  11. Ourselin, S., Roche, A., Pennec, X., Ayache, N.: Reconstructing a 3D structure from serial histological sections. Image Vis. Comput. 19(1), 25–31 (2001)

    Article  Google Scholar 

  12. Papachristidis, A., et al.: Clinical expert delineation of 3D left ventricular echocardiograms for the CETUS segmentation challenge. In: Proceedings of MICCAI CETUS Challenge, pp. 9–16 (2014)

    Google Scholar 

  13. Rajpoot, K., Grau, V., Alison Noble, J., Becher, H., Szmigielski, C.: The evaluation of single-view and multi-view fusion 3D echocardiography using image-driven segmentation and tracking. MedIA 15(4), 514–528 (2011)

    Google Scholar 

  14. Rueckert, D., Sonoda, L., Hayes, C., Hill, D.L., Leach, M., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imag. 18(8), 712–721 (1999)

    Article  Google Scholar 

  15. Stralen, M.V., Haak, A., Leung, K., Burken, G.V., Bosch, J.: Segmentation of multi-center 3D left ventricular echocardiograms by active appearance models. In: Proceedings of MICCAI CETUS Challenge, pp. 73–80 (2014)

    Google Scholar 

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Correspondence to Ozan Oktay .

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Oktay, O. et al. (2015). Probabilistic Edge Map (PEM) for 3D Ultrasound Image Registration and Multi-atlas Left Ventricle Segmentation. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds) Functional Imaging and Modeling of the Heart. FIMH 2015. Lecture Notes in Computer Science(), vol 9126. Springer, Cham. https://doi.org/10.1007/978-3-319-20309-6_26

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  • DOI: https://doi.org/10.1007/978-3-319-20309-6_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20308-9

  • Online ISBN: 978-3-319-20309-6

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