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Revisiting Contour-Driven and Knowledge-Based Deformable Models: Application to 2D-3D Proximal Femur Reconstruction from X-ray Images

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (MICCAI 2021)


In many clinical applications, 3D reconstruction of patient-specific structures is of major interest. Despite great effort put in 2D-3D reconstruction, gold standard bone reconstruction obtained by segmentation on CT images is still mostly used – at the expense of exposing patients to significant ionizing radiation and increased health costs. State-of-the-art 2D-3D reconstruction methods are based on non-rigid registration of digitally reconstructed radiographs (DRR) – aiming at full automation – but with varying accuracy often exceeding clinical requirements. Conversely, contour-based approaches can lead to accurate results but strongly depend on the quality of extracted contours and have been left aside in recent years. In this study, we revisit a patient-specific 2D-3D reconstruction method for the proximal femur based on contours, image cues, and knowledge-based deformable models. 3D statistical shape models were built using 199 CT scans from THA patients that were used to generate pairs of high fidelity DRRs. Convolutional neural networks were trained using the DRRs to investigate automatic contouring. Experiments were conducted on the DRRs, and calibrated radiographs of a pelvis phantom and volunteers – with an analysis of the quality of contouring and its automatization. Using manual contours and DRR, the best reconstruction error was 1.02 mm. With state-of-the-art results for 2D-3D reconstruction of the proximal femur, we highlighted the relevance and challenges of using contour-driven reconstruction to yield patient-specific models.

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    Calibration parameters include e.g., source-detector distance, pixel size, central point, relative transformations between multiple images, etc.


  1. Sarkalkan, N., Weinans, H., Zadpoor, A.A.: Statistical shape and appearance models of bones. Bone 60, 129–140 (2014).

    Article  Google Scholar 

  2. Gamage, P., Xie, S.Q., Delmas, P., Xu, W.L.: Diagnostic radiograph based 3D bone reconstruction framework: application to the femur. Comput. Med. Imaging Graph. 35(6), 427–437 (2011).

    Article  Google Scholar 

  3. Galibarov, P.E., Prendergast, P.J., Lennon, A.B.: A method to reconstruct patient-specific proximal femur surface models from planar pre-operative radiographs. Med. Eng. Phys. 32(10), 1180–1188 (2010).

    Article  Google Scholar 

  4. Markelj, P., Tomaževič, D., Likar, B., Pernuš, F.: A review of 3D/2D registration methods for image-guided interventions. Med. Image Anal. 16(3), 642–661 (2012).

    Article  Google Scholar 

  5. Reyneke, C.J.F., Lüthi, M., Burdin, V., Douglas, T.S., Vetter, T., Mutsvangwa, T.E.M.: Review of 2-D/3-D reconstruction using statistical shape and intensity models and X-ray image synthesis: toward a unified framework. IEEE Rev. Biomed. Eng. 12, 269–286 (2019).

    Article  Google Scholar 

  6. Karade, V., Ravi, B.: 3D femur model reconstruction from biplane X-ray images: a novel method based on Laplacian surface deformation. Int. J. Comput. Assist. Radiol. Surg. 10(4), 473–485 (2014).

    Article  Google Scholar 

  7. Zheng, G., Yu, W.: Chapter 12 - Statistical shape and deformation models based 2D–3D reconstruction. In: Zheng, G., Li, S., Székely, G. (eds.) Statistical Shape and Deformation Analysis, pp. 329–349. Academic Press (2017)

    Google Scholar 

  8. Klima, O., Kleparnik, P., Spanel, M., Zemcik, P.: Intensity-based femoral atlas 2D/3D registration using Levenberg-Marquardt optimization. In: Medical Imaging 2016: Biomedical Applications in Molecular Structural, and Functional Imaging, vol. 9788, p. 97880F (2016)

  9. Yu, W., Chu, C., Tannast, M., Zheng, G.: Fully automatic reconstruction of personalized 3D volumes of the proximal femur from 2D X-ray images. Int. J. Comput. Assist. Radiol. Surg. 11(9), 1673–1685 (2016).

    Article  Google Scholar 

  10. Baka, N., et al.: 2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Med. Image Anal. 15(6), 840–850 (2011).

    Article  Google Scholar 

  11. Zheng, G.: Personalized X-Ray reconstruction of the proximal femur via intensity-based non-rigid 2D-3D registration. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 598–606. Springer, Heidelberg (2011).

    Chapter  Google Scholar 

  12. Boussaid, H., Kadoury, S., Kokkinos, I., Lazennec, J.-Y., Zheng, G., Paragios, N.: 3D model-based reconstruction of the proximal femur from low-dose Biplanar X-Ray images. In: The 22nd British Machine Vision Conference - BMVC 2011, Dundee, United Kingdom, pp. 1–10, August 2011.

  13. Cerveri, P., Sacco, C., Olgiati, G., Manzotti, A., Baroni, G.: 2D/3D reconstruction of the distal femur using statistical shape models addressing personalized surgical instruments in knee arthroplasty: A feasibility analysis. Int. J. Med. Robot. Comput. Assisted Surgery 13(4), e1823 (2017).

    Article  Google Scholar 

  14. Youn, K., Park, M.S., Lee, J.: Iterative approach for 3D reconstruction of the femur from un-calibrated 2D radiographic images. Med. Eng. Phys. 50, 89–95 (2017).

    Article  Google Scholar 

  15. Wu, J., Mahfouz, M.R.: Reconstruction of knee anatomy from single-plane fluoroscopic x-ray based on a nonlinear statistical shape model. JMI 8(1), 016001 (2021).

    Article  Google Scholar 

  16. Mahfouz, M.R., Hoff, W.A., Komistek, R.D., Dennis, D.A.: Effect of segmentation errors on 3D-to-2D registration of implant models in X-ray images. J. Biomech. 38(2), 229–239 (2005).

    Article  Google Scholar 

  17. Grupp, R.B., et al.: Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration. Int. J. Comput. Assist. Radiol. Surg. 15(5), 759–769 (2020).

    Article  Google Scholar 

  18. Kasten, Y., Doktofsky, D., Kovler, I.: End-To-end convolutional neural network for 3D reconstruction of knee bones from bi-planar X-ray images. In: Deeba, F., Johnson, P., Würfl, T., Ye, J.C. (eds.) MLMIR 2020. LNCS, vol. 12450, pp. 123–133. Springer, Cham (2020).

    Chapter  Google Scholar 

  19. Livyatan, H., Yaniv, Z., Joskowicz, L.: Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT. IEEE Trans. Med. Imaging 22(11), 1395–1406 (2003).

    Article  Google Scholar 

  20. Damopoulos, D., et al.: Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration. Int. J. Comput. Assist. Radiol. Surg. 14(3), 545–561 (2019).

    Article  Google Scholar 

  21. Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2020).

    Article  Google Scholar 

  22. Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Dollar, P.: Designing network design spaces. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 10425–10433, June. 2020.

  23. Benameur, S., Mignotte, M., Parent, S., Labelle, H., Skalli, W., de Guise, J.: 3D/2D registration and segmentation of scoliotic vertebrae using statistical models. Comput. Med. Imaging Graph. 27(5), 321–337 (2003).

    Article  Google Scholar 

  24. Unberath, M., et al.: DeepDRR – a catalyst for machine learning in fluoroscopy-guided procedures. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 98–106. Springer, Cham (2018).

    Chapter  Google Scholar 

  25. Hataya, R., Zdenek, J., Yoshizoe, K., Nakayama, H.: Faster AutoAugment: learning augmentation strategies using Backpropagation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 1–16. Springer, Cham (2020).

    Chapter  Google Scholar 

  26. Kurazume, R., et al.: 3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images. Comput. Vis. Image Underst. 113(2), 202–211 (2009).

    Article  Google Scholar 

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This work was supported by the Swiss CTI project MyPlanner (no. 25258.1). Authors would like to thank E. Ambrosetti, A. Al-Musibli, L. Assassi and B. Lokaj at the Geneva School of Health Sciences and M. Bernardoni, M. Parravicini and D. Ascani at Medacta International SA.

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Correspondence to Christophe Chênes .

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Chênes, C., Schmid, J. (2021). Revisiting Contour-Driven and Knowledge-Based Deformable Models: Application to 2D-3D Proximal Femur Reconstruction from X-ray Images. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12906. Springer, Cham.

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