Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
149 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Boundary Constraint-free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction (2403.09964v4)

Published 15 Mar 2024 in eess.IV and cs.CV

Abstract: In image-guided liver surgery, 3D-3D non-rigid registration methods play a crucial role in estimating the mapping between the preoperative model and the intraoperative surface represented as point clouds, addressing the challenge of tissue deformation. Typically, these methods incorporate a biomechanical model, represented as a finite element model (FEM), into the strain energy term to regularize a surface matching term. We propose a 3D-3D non-rigid registration method that incorporates a modified FEM into the surface matching term. The modified FEM alleviates the need to specify boundary conditions, which is achieved by modifying the stiffness matrix of a FEM and using diagonal loading for stabilization. As a result, the modified surface matching term does not require the specification of boundary conditions or an additional strain energy term to regularize the surface matching term. Optimization is achieved through an accelerated gradient algorithm, further enhanced by our proposed method for determining the optimal step size. We evaluated our method and compared it to several state-of-the-art methods across various datasets. Our straightforward and effective approach consistently outperformed or achieved comparable performance to the state-of-the-art methods. Our code and datasets are available at https://github.com/zixinyang9109/BCF-FEM.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. J. S. Heiselman, W. R. Jarnagin, and M. I. Miga, “Intraoperative correction of liver deformation using sparse surface and vascular features via linearized iterative boundary reconstruction,” IEEE transactions on medical imaging, vol. 39, no. 6, pp. 2223–2234, 2020.
  2. J. N. Smit, K. F. Kuhlmann, B. R. Thomson, N. F. Kok, T. J. Ruers, and M. Fusaglia, “Ultrasound guidance in navigated liver surgery: toward deep-learning enhanced compensation of deformation and organ motion,” International journal of computer assisted radiology and surgery, pp. 1–9, 2023.
  3. I. Peterlik, H. Courtecuisse, R. Rohling, P. Abolmaesumi, C. Nguan, S. Cotin, and S. Salcudean, “Fast elastic registration of soft tissues under large deformations,” Medical image analysis, vol. 45, pp. 24–40, 2018.
  4. D. C. Rucker, Y. Wu, L. W. Clements, J. E. Ondrake, T. S. Pheiffer, A. L. Simpson, W. R. Jarnagin, and M. I. Miga, “A mechanics-based nonrigid registration method for liver surgery using sparse intraoperative data,” IEEE transactions on medical imaging, vol. 33, no. 1, pp. 147–158, 2013.
  5. S. Suwelack, S. Röhl, S. Bodenstedt, D. Reichard, R. Dillmann, T. dos Santos, L. Maier-Hein, M. Wagner, J. Wünscher, H. Kenngott, et al., “Physics-based shape matching for intraoperative image guidance,” Medical physics, vol. 41, no. 11, p. 111901, 2014.
  6. R. Modrzejewski, T. Collins, B. Seeliger, A. Bartoli, A. Hostettler, and J. Marescaux, “An in vivo porcine dataset and evaluation methodology to measure soft-body laparoscopic liver registration accuracy with an extended algorithm that handles collisions,” International journal of computer assisted radiology and surgery, vol. 14, no. 7, pp. 1237–1245, 2019.
  7. A. Mendizabal, E. Tagliabue, and D. Dall’Alba, “Intraoperative estimation of liver boundary conditions from multiple partial surfaces,” International Journal of Computer Assisted Radiology and Surgery, pp. 1–8, 2023.
  8. Z. Yang, R. A. Simon, and C. A. Linte, “Disparity refinement framework for learning-based stereo matching methods in cross-domain setting for laparoscopic images,” Journal of Medical Imaging, vol. 10, no. 4, p. 045001, 2023.
  9. J. S. Heiselman, L. W. Clements, J. A. Collins, J. A. Weis, A. L. Simpson, S. K. Geevarghese, T. P. Kingham, W. R. Jarnagin, and M. I. Miga, “Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery,” Journal of Medical Imaging, vol. 5, no. 2, pp. 021203–021203, 2018.
  10. D. Lee, W. H. Nam, J. Y. Lee, and J. B. Ra, “Non-rigid registration between 3d ultrasound and ct images of the liver based on intensity and gradient information,” Physics in Medicine & Biology, vol. 56, no. 1, p. 117, 2010.
  11. M. Labrunie, D. Pizarro, C. Tilmant, and A. Bartoli, “Automatic 3d/2d deformable registration in minimally invasive liver resection using a mesh recovery network,” in Medical Imaging with Deep Learning, 2023.
  12. Y. Espinel, L. Calvet, K. Botros, E. Buc, C. Tilmant, and A. Bartoli, “Using multiple images and contours for deformable 3d-2d registration of a preoperative ct in laparoscopic liver surgery,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 657–666, Springer, 2021.
  13. T. Collins, D. Pizarro, S. Gasparini, N. Bourdel, P. Chauvet, M. Canis, L. Calvet, and A. Bartoli, “Augmented reality guided laparoscopic surgery of the uterus,” IEEE Transactions on Medical Imaging, vol. 40, no. 1, pp. 371–380, 2020.
  14. B. Deng, Y. Yao, R. M. Dyke, and J. Zhang, “A survey of non-rigid 3d registration,” in Computer Graphics Forum, vol. 41, pp. 559–589, Wiley Online Library, 2022.
  15. J. Zhang, Y. Zhong, and C. Gu, “Deformable models for surgical simulation: a survey,” IEEE reviews in biomedical engineering, vol. 11, pp. 143–164, 2017.
  16. G. Mestdagh and S. Cotin, “An optimal control problem for elastic registration and force estimation in augmented surgery,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VII, pp. 74–83, Springer, 2022.
  17. S. Khallaghi, C. A. Sánchez, A. Rasoulian, Y. Sun, F. Imani, A. Khojaste, O. Goksel, C. Romagnoli, H. Abdi, S. Chang, et al., “Biomechanically constrained surface registration: Application to mr-trus fusion for prostate interventions,” IEEE transactions on medical imaging, vol. 34, no. 11, pp. 2404–2414, 2015.
  18. A. Myronenko and X. Song, “Point set registration: Coherent point drift,” IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 12, pp. 2262–2275, 2010.
  19. G. Mestdagh, “An optimal control formulation for organ registration in augmented surgery,” 2022.
  20. M. Pfeiffer, C. Riediger, S. Leger, J.-P. Kühn, D. Seppelt, R.-T. Hoffmann, J. Weitz, and S. Speidel, “Non-rigid volume to surface registration using a data-driven biomechanical model,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 724–734, Springer, 2020.
  21. E. Tagliabue, D. Dall’Alba, M. Pfeiffer, M. Piccinelli, R. Marin, U. Castellani, S. Speidel, and P. Fiorini, “Data-driven intra-operative estimation of anatomical attachments for autonomous tissue dissection,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1856–1863, 2021.
  22. E. L. Brewer, L. W. Clements, J. A. Collins, D. J. Doss, J. S. Heiselman, M. I. Miga, C. D. Pavas, and E. H. Wisdom III, “The image-to-physical liver registration sparse data challenge,” in Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10951, pp. 364–370, SPIE, 2019.
  23. B. Acidi, M. Ghallab, S. Cotin, E. Vibert, and N. Golse, “Augmented reality in liver surgery, where we stand in 2023,” Journal of Visceral Surgery, 2023.
  24. N. Golse, A. Petit, M. Lewin, E. Vibert, and S. Cotin, “Augmented reality during open liver surgery using a markerless non-rigid registration system,” Journal of Gastrointestinal Surgery, vol. 25, pp. 662–671, 2021.
  25. P. J. Besl and N. D. McKay, “Method for registration of 3-d shapes,” in Sensor fusion IV: control paradigms and data structures, vol. 1611, pp. 586–606, Spie, 1992.
  26. L. W. Clements, W. C. Chapman, B. M. Dawant, R. L. Galloway Jr, and M. I. Miga, “Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation,” Medical physics, vol. 35, no. 6Part1, pp. 2528–2540, 2008.
  27. J. Bonet and R. D. Wood, Nonlinear continuum mechanics for finite element analysis. Cambridge university press, 1997.
  28. Y. Nesterov, “A method for unconstrained convex minimization problem with the rate of convergence,” 1983.
  29. Z. Yang, R. Simon, and C. A. Linte, “Learning feature descriptors for pre-and intra-operative point cloud matching for laparoscopic liver registration,” International Journal of Computer Assisted Radiology and Surgery, pp. 1–8, 2023.
  30. M. R. Robu, J. Ramalhinho, S. Thompson, K. Gurusamy, B. Davidson, D. Hawkes, D. Stoyanov, and M. J. Clarkson, “Global rigid registration of ct to video in laparoscopic liver surgery,” International Journal of Computer Assisted Radiology and Surgery, vol. 13, no. 6, pp. 947–956, 2018.
  31. N. Parikh and S. Boyd, “Proximal algorithms,” Foundations and trends® in Optimization, vol. 1, no. 3, pp. 127–239, 2014.
  32. H. Jeffreys and B. Jeffreys, Methods of mathematical physics. Cambridge university press, 1999.
  33. “Use soft spring to stabilize model.” https://help.solidworks.com/2021/english/SolidWorks/cworks/c_Use_Soft_Spring_to_Stabilize_Model.htm, 2021.
  34. H. Kenngott, J. Wünscher, M. Wagner, A. Preukschas, A. Wekerle, P. Neher, S. Suwelack, S. Speidel, F. Nickel, D. Oladokun, et al., “Openhelp (heidelberg laparoscopy phantom): development of an open-source surgical evaluation and training tool,” Surgical endoscopy, vol. 29, pp. 3338–3347, 2015.
  35. S. Hang, “Tetgen, a delaunay-based quality tetrahedral mesh generator,” ACM Trans. Math. Softw, vol. 41, no. 2, p. 11, 2015.
  36. Q.-Y. Zhou, J. Park, and V. Koltun, “Open3d: A modern library for 3d data processing,” arXiv preprint arXiv:1801.09847, 2018.
  37. J. A. Collins, J. A. Weis, J. S. Heiselman, L. W. Clements, A. L. Simpson, W. R. Jarnagin, and M. I. Miga, “Improving registration robustness for image-guided liver surgery in a novel human-to-phantom data framework,” IEEE transactions on medical imaging, vol. 36, no. 7, pp. 1502–1510, 2017.
  38. G. Guennebaud, B. Jacob, et al., “Eigen v3.” http://eigen.tuxfamily.org, 2010.
  39. J. L. Sparks and R. B. Dupaix, “Constitutive modeling of rate-dependent stress–strain behavior of human liver in blunt impact loading,” Annals of biomedical engineering, vol. 36, pp. 1883–1892, 2008.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets