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A personalized insertion centers preoperative positioning method for minimally invasive surgery of cruciate ligament reconstruction

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Abstract

In the surgery of knee cruciate ligament repair, how to accurately and personalized obtain cruciate ligament insertion centers is the key issue and a great challenge for surgeons. Current artificial judgment is often with deviation, which increases the risk of surgical failure, complications, and the need for a second surgery. Surgical failure can cause imbalanced force on the knee joint (even limping). In this paper, we propose a personalized preoperative positioning method framework of cruciate ligament insertion centers. We focus on locating the insertion centers and verifying the accuracy of the results. The main steps of the method are as follows. First, we propose a new network model W-UNet to better segment accurate bone regions and small ligament regions from MRI. Second, based on RANSAC algorithm and ICP algorithm, MRI bone model and CT bone model are registered. Third, based on the extracted bone and ligament models, we propose an accurate and personalized method for locating the insertion centers. Fourth, we propose a method based on the principle of calculus, using broken lines instead of curves, to solve the problem of ligament reconstruction blocked by intercondylar protrusions. Finally, the length of cruciate ligament with different flexion angles verifies the accuracy of insertion centers. Preoperative personalized insertion centers preoperative positioning can be performed according to the patient’s MRI and CT images. Validation experiments proved the accuracy and robustness of this method. Surgeons can use this framework to accurately obtain personalized preoperative insertion centers location for target patients. This framework provides a reasonable and feasible technical means for locating and marking cruciate ligament insertion centers. It effectively solves the problem of difficult ligament centers localization in clinical surgery and reduces the risk of surgical failure.

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Data Availability Statement

The datasets generated during the current study are available from the corresponding author on reasonable request.

References

  1. Razali, M. H., Sazwan, S. M., Mahmood, M., et al.: Anterior cruciate ligament (ACL) coronal view injury diagnosis system using convolutional neural network. In: Proceedings of the 2019 2nd International Conference on Electronics and Electrical Engineering Technology, pp 118–122, 2019.

  2. Prince, M.R., Stuart, M.J., King, A.H., et al.: All-inside posterior cruciate ligament reconstruction: Graftlink technique. Arthrosc. Tech. 4(5), e619–e624 (2015)

    Article  Google Scholar 

  3. Rao, Z., Zhou, C., Kernkamp, W.A., et al.: In vivo kinematics and ligamentous function of the knee during weight-bearing flexion: an investigation on mid-range flexion of the knee. Knee Surg. Sports Traumatol. Arthrosc. 28(3), 797–805 (2019)

    Article  Google Scholar 

  4. Krogsgaard, M.R., Fischer-Rasmussen, T., Dyhre-Poulsen, P.: Absence of sensory function in the reconstructed anterior cruciate ligament. J. Electromyogr. Kinesiol. 21(1), 82–86 (2011)

    Article  Google Scholar 

  5. Lynch, T.S., Parker, R.D., Patel, R.M., et al.: The Impact of the Multicenter Orthopaedic Outcomes Network (MOON) research on anterior cruciate ligament reconstruction and orthopaedic practice. J. Am. Acad. Orthop. Surg. 23(3), 154–163 (2015)

    Article  Google Scholar 

  6. Tiamklang, T., Sumanont, S., Foocharoen, T., et al. Double-bundle versus single-bundle reconstruction for anterior cruciate ligament rupture in adults. Cochrane Database Systematic Rev. 11(11), :CD008413 (2012).

  7. Munch, D., Hansen, T.I., Mikkelsen, K.L., et al.: Complications and technical failures are rare in knee ligament reconstruction: analyses based on 31,326 reconstructions during 10 years in Denmark. Knee Surg. Sports Traumatol. Arthrosc. 27(8), 2672–2679 (2019)

    Article  Google Scholar 

  8. Marchant, B.G., Noyes, F.R., Barber-Westin, S.D., et al.: Prevalence of nonanatomical graft placement in a series of failed anterior cruciate ligament reconstructions. Am. J. Sports Med. 38(10), 1987–1996 (2010)

    Article  Google Scholar 

  9. Kohn, D., Rupp, S.: Strategies for revision anterior cruciate ligament reconstruction. Chirurg 71(9), 1055–1065 (2000)

    Article  Google Scholar 

  10. Weiler, A., Jung, T.M., Lubowicki, A., et al.: Management of posterior cruciate ligament reconstruction after previous isolated anterior cruciate ligament reconstruction. Arthroscopy J. Arthroscopic Related Surg. 23(2), 164–169 (2007)

    Article  Google Scholar 

  11. Christino, M.A., Fantry, A.J., Vopat, B.G.: Psychological aspects of recovery following anterior cruciate ligament reconstruction. J. Am. Acad. Orthop. Surg. 23(8), 501 (2015)

    Article  Google Scholar 

  12. Jia, H. H., Wen, Z. L., Seah, C. L., et al. Anterior cruciate ligament segmentation: using morphological operations with active contour. In: 2010 4th International Conference on Bioinformatics and Biomedical Engineering, pp 2209–2212, 2010.

  13. Zarychta, P.: ACL and PCL of the knee joint in the computer diagnostics. In: 21st International Conference "Mixed Design of Integrated Circuits and Systems" (MIXDES 2014), pp 489–492, 2014.

  14. Vinay, N. A,, Vinay, H. C., Narendra, T. V.: An active contour method for mr image segmentation of anterior cruciate ligament (ACL). In: 2014 Fifth international conference on signal and image processing (ICSIP 2014), pp 142–147, 2014.

  15. Lee, H., Hong, H., et al.: Anterior cruciate ligament segmentation from knee MR images using graph cuts with shape priors. J. KISS: Softw. Appl. 41(1), 36–45 (2014)

    Google Scholar 

  16. Lee, H., Hong, H., et al.: Anterior cruciate ligament segmentation from knee MR images using graph cuts with geometric and probabilistic shape constraints. In: Proceedings of the 11th Asian conference on Computer Vision—Volume Part II. 2012:305–315, 2015.

  17. Lee, H., Hong, H., et al.: Segmentation of anterior cruciate ligament in knee MR images using graph cuts with patient-specific shape constraints and label refinement. Comput. Biol. Med. 55, 1–10 (2014)

    Article  Google Scholar 

  18. Gudodagi, R.: Segmentation of ACL in MR images. Int. J. Eng. Comp. Sci. 2(6), 2033–2036 (2013)

    Google Scholar 

  19. Flannery, S.W., Kiapour, A.M., Edgar, D.J., et al.: A transfer learning approach for automatic segmentation of the surgically treated anterior cruciate ligament. J. Orthop. Res. 40(1), 277–284 (2021)

    Article  Google Scholar 

  20. Flannery, S.W., Kiapour, A.M., Edgar, D.J., et al.: Automated magnetic resonance image segmentation of the anterior cruciate ligament. J. Orthop. Res. 39(4), 831–840 (2020)

    Article  Google Scholar 

  21. Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. Med. Image Comp. Comp.-Assist. Intervent, pp 234–241, 2015.

  22. Zheng, J., Ji, Z., Yu, K., et al.: A feature-based solution for 3D registration of CT and MRI images of human knee. SIViP 9(8), 1815–1824 (2015)

    Article  Google Scholar 

  23. Campanelli, V., Howell, S.M., Hull, M.L.: Morphological errors in 3D bone models of the distal femur and proximal tibia generated from magnetic resonance imaging and computed tomography determined using two registration methods. Comp. Methods Biomech. Biomed. Eng. Imag. Visualization 8(1), 31–39 (2019)

    Article  Google Scholar 

  24. Yang, D., Chen, N., Tang, Q., et al.: Research on defect detection of toy sets based on an improved U-Net. Vis. Comput. (2023). https://doi.org/10.1007/s00371-023-02834-w

    Article  Google Scholar 

  25. Üzen, H., Turkoglu, M., Aslan, M., et al.: Depth-wise squeeze and excitation block-based efficient-unet model for surface defect detection. Vis. Comput. 39, 1745–1764 (2023)

    Article  Google Scholar 

  26. Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: Transformer for Semantic Segmentation. In: IEEE/CVF International Conference on Computer Vision, pp 7242–7252, 2021.

  27. Fu, J., Liu, J., Tian, H., et al.: Dual attention network for scene segmentation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 3146–3154, 2019.

  28. Azad, R., Asadi-Aghbolaghi, M., Fathy, M., Escalera, S.: Bi-directional ConvLSTM U-Net with densley connected convolutions. In: IEEE/CVF International Conference on Computer Vision Workshops, pp 27–28, 2019.

  29. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  30. Besl, P.J., Mckay, H.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  31. Rusu, R.B., Blodow, N., Beetz, M.: Fast Point Feature Histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation, pp 3212–3217 (2009)

  32. Wang, C., Xu, Y., Wang, L., et al.: Fast structural global registration of indoor colored point cloud. Vis. Comput. 38, 4279–4290 (2022)

    Article  Google Scholar 

  33. Dong, K., Gao, S., Xin, S., et al.: Probability driven approach for point cloud registration of indoor scene. Vis. Comput. 38, 51–63 (2022)

    Article  Google Scholar 

  34. Tao, W., Hua, X., He, X., et al.: Automatic multi-view registration of point clouds via a high-quality descriptor and a novel 3D transformation estimation technique. Vis. Comput. (2023). https://doi.org/10.1007/s00371-023-02942-7

    Article  Google Scholar 

  35. Sun, X.B., Zhang, K.Y., Gang, L.I., et al.: Measurement of anatomical sites to locate the center of reconstruction of the anterior cruciate ligament of knee flexion angle ligament length and its significance. China J. Endosc. 46(4), 453–455 (2014)

    Google Scholar 

  36. Rong, K., Wang, H.P., Wang, Y., et al.: 3D dynamic research on spatial lengths of functional bundles in knee cruciate ligaments. J. Med. Biomech. 29(4), 339–345 (2014)

    Google Scholar 

  37. Choi, H.F., Chincisan, A., Becker, M., et al.: Multimodal composition of the digital patient: a strategy for the knee articulation. Vis. Comput. 30, 739–749 (2014)

    Article  Google Scholar 

  38. Rasool, S., Sourin, A.: Image-driven virtual simulation of arthroscopy. Vis. Comput. 29, 333–344 (2013)

    Article  Google Scholar 

  39. Lin, Q., Yang, R., Cai, K., et al.: Strategy for accurate liver intervention by an optical tracking system. Biomed. Opt. Express 6(9), 3287–3302 (2015)

    Article  Google Scholar 

  40. Zheng, L., Wu, H., Yang, L., et al.: A novel respiratory follow-up robotic system for thoracic-abdominal puncture. IEEE Trans. Industr. Electron. 68(3), 2368–2378 (2021)

    Article  Google Scholar 

  41. Gulabi, D., Erdem, M., et al.: Neglected patellar tendon rupture with anterior cruciate ligament rupture and medial collateral ligament partial rupture. Acta Orthopaedica Et Traumatol. Turcica, 48(2), 231–235 (2014).

  42. Lin, Q., Cai, K., Yang, R., et al.: Development and validation of a near-infrared optical system for tracking surgical instruments. J. Med. Syst. 40(4), 107 (2016)

    Article  Google Scholar 

  43. Jacobson, K.E., Chi, F.S.: Evaluation and treatment of medial collateral ligament and medial-sided injuries of the knee. Sports Med. Arthrosc. Rev. 14(2), 58–66 (2006)

    Article  Google Scholar 

  44. Grawe, B. S. et al.: Lateral collateral ligament injury about the knee: anatomy, evaluation, and management. J. Am. Acad. Orthop. Surg. 26(6), e120–e127 (2018)

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Nos. 62372079, 61972440 and 61572101), the Fundamental Research Funds for the Central Universities of China (No. DUT22YG104), the National Natural Science Foundation of Liaoning Province of China (No. 2021-YGJC-23), the Scientific Research Project of Educational Department of Liaoning Province of China (No. LZ2020031), and the Key Research and Development Projects of Liaoning Province of China (No. 2021JH2/10300025). The author would like to thank the volunteers for providing images.

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Correspondence to Liang Yang or Bin Liu.

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Liu, H., Li, P., Liu, D. et al. A personalized insertion centers preoperative positioning method for minimally invasive surgery of cruciate ligament reconstruction. Vis Comput 40, 3937–3960 (2024). https://doi.org/10.1007/s00371-024-03399-y

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