Automated tumor localization via synergistic liver surface and vascular constraints deformation

Abstract

Open tumor resection is one of the most commonly used treatments for malignant liver tumors. The ability to accurately locate the liver tumor during the operation is the key to the success of the operation. Intraoperative liver tumor localization remains challenging due to tissue deformation and intraoperative imaging limitations. This paper proposes a dual-constraint framework that synergistically integrates liver surface deformation and vascular biomechanical modeling to resolve this problem. Liver surface registration captures global deformation using a fast finite-element model (18 s), while vascular topology matching refines internal tumor displacement by enforcing correspondence between preoperative and intraoperative vessel trees. This synergistic strategy leverages both external and internal anatomical cues to achieve robust localization. Evaluated on 13 clinical cases, our method achieved sub-millimeter tumor localization accuracy (1.68±0.22 mm). Compared to single-constraint methods (LTLS: 2.04±0.26 mm; LTBV: 2.23±0.31 mm), our approach reduced error by 24%–37% without increasing runtime. This clinically efficient method shows promise for improving intraoperative guidance during liver tumor ablation.

Keywords

Liver tumor localization / Biomechanical model / 3D registration / Cooperative constraint / Deformation

References

[1] Hitoshi Maruyama, Maki Tobari, Hiroaki Nagamatsu, Tadashi Yamaguchi, Shuichiro Shiina, Ablation for benign liver tumors: current concepts and limitations, J. Clin. Transl. Hepatol. 11 (1) (2022) 244.

[2] Omar Ibrahim Alirr, Ashrani Aizzuddin Abd Rahni, Survey on liver tumour resection planning system: steps, techniques, and parameters, J. Digit. Imaging 33 (2) (2020) 304-323.

[3] Catherine G. Tran, Scott K. Sherman, Chandrikha Chandrasekharan, James R. Howe, Surgical management of neuroendocrine tumor liver metastases, Hematology/Oncology Clin. 39 (1) (2025) 37-53.

[4] Chengfang Wang, Linjie Song, Zhijiang Wang, Weilin Wang, The application of radiofrequency ablation in pancreatic cancer liver-only recurrence after radical pancreatectomy, Med. Oncol. 40 (7) (2023) 209.

[5] E. Rio, F. Mornex, P. Maingon, D. Peiffert, L. Parent, Hepatic tumours and radiotherapy, Cancer/Radiothérapie 26 (1–2) (2022) 266-271.

[6] Ahmed Younos, Melissa Touadi, Sharona Ross, Iswanto Sucandy, Open parenchymal sparing posterosuperior liver tumor resection for colorectal liver metastasis with proximity to hepatic veins, Am. Surg.™ 89 (8) (2023) 3609-3611.

[7] Shreya Bharat Shah, Shashank Pandey, Akhil Kumar, Amitabh Dutta, Jayashree Sood, Anesthesia considerations in combined open heart cardiac myofibroblastic tumor excision and living donor liver transplantation, J. Cardiothorac. Vasc. Anesth. 34 (4) (2020) 1010-1014.

[8] Eliana M. Vásquez Osorio, Mischa S. Hoogeman, Alejandra Méndez Romero, Piotr Wielopolski, András Zolnay, Ben J.M. Heijmen, Accurate CT/MR vessel-guided nonrigid registration of largely deformed livers, Med. Phys. 39 (5) (2012) 2463-2477.

[9] Jon S. Heiselman, Logan W. Clements, Jarrod A. Collins, Jared A. Weis, Amber L. Simpson, Sunil K. Geevarghese, T. Peter Kingham, William R. Jarnagin, Michael I. Miga, Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery, J. Med. Imaging, 5 (2) (2018) 021203-021203.

[10] Erol Özgür, Bongjin Koo, Bertrand Le Roy, Emmanuel Buc, Adrien Bartoli, Preoperative liver registration for augmented monocular laparoscopy using backward–forward biomechanical simulation, Int. J. Comput. Assist. Radiol. Surg. 13 (2018) 1629-1640.

[11] Richard Modrzejewski, Toby Collins, Barbara Seeliger, Adrien Bartoli, Alexandre Hostettler, Jacques Marescaux, An in vivo porcine dataset and evaluation methodology to measure soft-body laparoscopic liver registration accuracy with an extended algorithm that handles collisions, Int. J. Comput. Assist. Radiol. Surg. 14 (2019) 1237-1245.

[12] Bo Lu, et al., Endo-4SRF: Learning radiance field for dynamic surface reconstruction of surgical tissues with obstacle stealth under single-view and depth-free monocular endoscopy, IEEE J. Biomed. Heal. Inform. (2024).

[13] Bo Lu, et al., M3-DEGREES net: Monocular-guided metric marching depth estimation with graph-based relevance ensemble for endoluminal surgery, IEEE J. Biomed. Heal. Inform. (2025).

[14] Logan W. Clements, Jarrod A. Collins, Jared A. Weis, Amber L. Simpson, T. Peter Kingham, William R. Jarnagin, Michael I. Miga, Deformation correction for image guided liver surgery: An intraoperative fidelity assessment, Surgery 162 (3) (2017) 537-547.

[15] Ha Manh Luu, Wiro Niessen, Theo Van Walsum, Camiel Klink, Adriaan Moelker, An automatic registration method for pre-and post-interventional CT images for assessing treatment success in liver RFA treatment, Med. Phys. 42 (9) (2015) 5559-5567.

[16] Hassan Boulkhrif, Ha Manh Luu, Theo van Walsum, Adriaan Moelker, Accuracy of semi-automated versus manual localisation of liver tumours in CT-guided ablation procedures, Eur. Radiol. 28 (2018) 4978-4984.

[17] Jon S. Heiselman, William R. Jarnagin, Michael I. Miga, Intraoperative correction of liver deformation using sparse surface and vascular features via linearized iterative boundary reconstruction, IEEE Trans. Med. Imaging 39 (6) (2020) 2223-2234.

[18] Taeyong Park, Jeongjin Lee, Juneseuk Shin, Kyoung Won Kim, Ho Chul Kang, Non-rigid liver registration in liver computed tomography images using elastic method with global and local deformations, J. Med. Imaging Heal. Inform. 11 (3) (2021) 810-816.

[19] Jiahe Chen, Kazuaki Hara, Etsuko Kobayashi, Ichiro Sakuma, Naoki Tomii, Occlusion-robust scene flow-based tissue deformation recovery incorporating a mesh optimization model, Int. J. Comput. Assist. Radiol. Surg. 18 (6) (2023) 1043-1051.

[20] Tian Xu, Yong Lei, XiaoLiang Cheng, Murong Li, Identification of Young’s modulus and equivalent spring constraint boundary conditions of the soft tissue with locally observed displacements for endoscopic liver surgery, Comput. Methods Biomech. Biomed. Eng. 25 (4) (2022) 439-454.

[21] Daniel Rueckert, Luke I. Sonoda, Carmel Hayes, Derek L.G. Hill, Martin O. Leach, David J. Hawkes, Nonrigid registration using free-form deformations: application to breast MR images, IEEE Trans. Med. Imaging 18 (8) (1999) 712-721.

[22] Wen-Chi Christina Lee, Mitchell E. Tublin, Brian E. Chapman, Registration of MR and CT images of the liver: comparison of voxel similarity and surface based registration algorithms, Comput. Methods Programs Biomed. 78 (2) (2005) 101-114.

[23] Panlong Xu, Chao Chen, Xinyi Wang, Wentao Li, Jianqi Sun, ROI-based intraoperative MR-CT registration for image-guided multimode tumor ablation therapy in hepatic malignant tumors, IEEE Access 8 (2020) 13613-13619.

[24] Wei Wei, Xu Haishan, Julian Alpers, Marko Rak, Christian Hansen, A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation, Comput. Methods Programs Biomed. 206 (2021) 106117.

[25] Ha Manh Luu, Wiro Niessen, Theo Van Walsum, Camiel Klink, Adriaan Moelker, An automatic registration method for pre-and post-interventional CT images for assessing treatment success in liver RFA treatment, Med. Phys. 42 (9) (2015) 5559-5567.

[26] Eldad Haber, Jan Modersitzki, Numerical methods for volume preserving image registration, Inverse Problems 20 (5) (2004) 1621.

[27] Andrea Mendizabal, Eleonora Tagliabue, Diego Dall’Alba, Intraoperative estimation of liver boundary conditions from multiple partial surfaces, Int. J. Comput. Assist. Radiol. Surg. 18 (7) (2023) 1295-1302.

[28] Guillaume Mestdagh, Stéphane Cotin, An optimal control problem for elastic registration and force estimation in augmented surgery, International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2022, pp. 74-83.

[29] Sergei Nikolaev, Stéphane Cotin, Estimation of boundary conditions for patient-specific liver simulation during augmented surgery, Int. J. Comput. Assist. Radiol. Surg. 15 (2020) 1107-1115.

[30] Tian Xu, Zhen Wang, Yingda Hu, Shilun Du, Ao Du, Zhenyang Yu, Yong Lei, A FEM-based direct method for identification of Young’s modulus and boundary conditions in three-dimensional linear elasticity from local observation, Int. J. Mech. Sci. 237 (2023) 107797.

[31] David M. Cash, Michael I. Miga, Tuhin K. Sinha, Robert L. Galloway, William C. Chapman, Compensating for intraoperative soft-tissue deformations using incomplete surface data and finite elements, IEEE Trans. Med. Imaging 24 (11) (2005) 1479-1491.

[32] Jon S. Heiselman, Michael I. Miga, The image-to-physical liver registration sparse data challenge: characterizing inverse biomechanical model resolution, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 11315, SPIE, 2020, pp. 371-377.

[33] Jon S. Heiselman, William R. Jarnagin, Michael I. Miga, Intraoperative correction of liver deformation using sparse surface and vascular features via linearized iterative boundary reconstruction, IEEE Trans. Med. Imaging 39 (6) (2020) 2223-2234.

[34] E. Lee Brewer, Logan W. Clements, Jarrod A. Collins, Derek J. Doss, Jon S. Heiselman, Michael I. Miga, Chris D. Pavas, Edward H. Wisdom III, The image-to-physical liver registration sparse data challenge, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10951, SPIE, 2019, pp. 364-370.

[35] Joao Ramalhinho, Henry F.J. Tregidgo, Kurinchi Gurusamy, David J. Hawkes, Brian Davidson, Matthew J. Clarkson, Registration of untracked 2D laparoscopic ultrasound to CT images of the liver using multi-labelled content-based image retrieval, IEEE Trans. Med. Imaging 40 (3) (2020) 1042-1054.

[36] Longfei Ma, Hanying Liang, Boxuan Han, Shizhong Yang, Xinran Zhang, Hongen Liao, Augmented reality navigation with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors, Int. J. Comput. Assist. Radiol. Surg. 17 (9) (2022) 1543-1552.

[37] Shuwei Xing, Derek W. Cool, Lori Gardi, Jeffrey Bax, Elvis C.S. Chen, Terry M. Peters, Aaron Fenster, A 2D/3D US/CT-guided system for percutaneous focal liver thermal ablation, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 12034, SPIE, 2022, pp. 237-245.

[38] Jane M. Blackall, Graeme P. Penney, Andrew P. King, Andreas N. Adam, David J. Hawkes, Tracking alignment of sparse ultrasound with preoperative images of the liver and an interventional plan using models of respiratory motion and deformation, Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, vol. 5367, SPIE, 2004, pp. 218-227.

[39] Wolfgang Wein, Shelby Brunke, Ali Khamene, Matthew R. Callstrom, Nassir Navab, Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention, Med. Image Anal. 12 (5) (2008) 577-585.

[40] Jaime Garcia Guevara, Igor Peterlik, Marie-Odile Berger, Stephane Cotin, Biomechanics-based graph matching for augmented CT-CBCT, Int. J. Comput. Assist. Radiol. Surg. 13 (2018) 805-813.

[41] Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jäger, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso, Elastic registration of geodesic vascular graphs, Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16–20, 2018, Proceedings, Part I, Springer, 2018, pp. 810-818.

[42] Hui Xue, Christina Malamateniou, Joanna Allsop, Latha Srinivasan, Joseph V. Hajnal, Daniel Rueckert, Automatic extraction and matching of neonatal cerebral vasculature, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006, IEEE, 2006, pp. 125-128.

[43] F. Nazem, Alireza Ahmadian, N. Dadashi Seraj, Masoomeh Giti, Two-stage point-based registration method between ultrasound and CT imaging of the liver based on ICP and unscented Kalman filter: a phantom study, Int. J. Comput. Assist. Radiol. Surg. 9 (2014) 39-48.

[44] Chaity Biswas, Rehena Nasrin, Muhammad S. Ahmad, Numerical analogy of bioheat transfer and microwave cancer therapy for liver tissue, Heat Transf. 51 (7) (2022) 6403-6430.

[45] Christopher P. Lee, Zhoubing Xu, Ryan P. Burke, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman, Evaluation of five image registration tools for abdominal CT: pitfalls and opportunities with soft anatomy, Proceedings of Spie–the International Society for Optical Engineering, vol. 9413, NIH Public Access, 2015.

[46] Eduard Serradell, Miguel Amável Pinheiro, Raphael Sznitman, Jan Kybic, Francesc Moreno-Noguer, Pascal Fua, Non-rigid graph registration using active testing search, IEEE Trans. Pattern Anal. Mach. Intell. 37 (3) (2014) 625-638.

[47] Delia Lorente, Francisco Martínez-Martínez, María José Rupérez, Miguel A. Lago, Marcelino Martínez-Sober, Pablo Escandell-Montero, José María Martínez-Martínez, Sandra Martínez-Sanchis, Antonio J. Serrano-López, C. Monserrat, et al., A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning, Expert Syst. Appl. 71 (2017) 342-357.

[48] Matthieu Nesme, Yohan Payan, François Faure, Efficient, physically plausible finite elements, Eurographics, 2005.

[49] You Zhang, Michael R. Folkert, Xiaokun Huang, Lei Ren, Jeffrey Meyer, Joubin Nasehi Tehrani, Robert Reynolds, Jing Wang, Enhancing liver tumor localization accuracy by prior-knowledge-guided motion modeling and a biomechanical model, Quant. Imaging Med. Surg. 9 (7) (2019) 1337.

[50] Chang-Min Lee, Min-Young Jeong, Sam-Youl Yoon, Laparoscopic liver resection enhanced by an intervention-guided fluorescence imaging technique using sodium fluorescein, J. Clin. Med. 10 (16) (2021) 3663.

[51] Hui Jun Lim, Adrian Kah Heng Chiow, Lip Seng Lee, Siong San Tan, Brian K.P. Goh, Ye Xin Koh, Chung Yip Chan, Ser Yee Lee, Novel method of intraoperative liver tumour localisation with indocyanine green and near-infrared imaging, Singapore Med. J. 62 (4) (2021) 182.

[52] Yoshinobu Shimohigashi, Ryo Toya, Tetsuo Saito, Osamu Ikeda, Masato Maruyama, Keisuke Yonemura, Yuji Nakaguchi, Yudai Kai, Yasuyuki Yamashita, Natsuo Oya, et al., Tumor motion changes in stereotactic body radiotherapy for liver tumors: an evaluation based on four-dimensional cone-beam computed tomography and fiducial markers, Radiat. Oncol. 12 (2017) 1-8.

[53] Jennifer Dhont, Jef Vandemeulebroucke, Manuela Burghelea, Kenneth Poels, Tom Depuydt, Robbe Van Den Begin, Cyril Jaudet, Christine Collen, Benedikt Engels, Truus Reynders, et al., The long-and short-term variability of breathing induced tumor motion in lung and liver over the course of a radiotherapy treatment, Radiother. Oncol. 126 (2) (2018) 339-346.

[54] Hua-Chieh Shao, Jing Wang, Ti Bai, Jaehee Chun, Justin C. Park, Steve Jiang, You Zhang, Real-time liver tumor localization via a single X-ray projection using deep graph neural network-assisted biomechanical modeling, Phys. Med. Biol. 67 (11) (2022) 115009.

[55] Anil Gupta, Rishabh Kumar, Hanuman Prasad Yadav, Manik Sharma, Rose Kamal, Deepak Thaper, Prabir Banik, Shipra Gupta, Kartik Saroha, Sandeep Singh, et al., Feasibility of 4D CT simulation with synchronized intravenous contrast injection in hepatocellular carcinoma, Rep. Pr. Oncol. Radiother. 25 (2) (2020) 293-298.