Real-time navigation for laparoscopic hepatectomy using image fusion of preoperative 3D surgical plan and intraoperative indocyanine green fluorescence imaging



Understanding the internal anatomy of the liver remains a major challenge in anatomical liver resection. Although virtual hepatectomy and indocyanine green (ICG) fluorescence imaging techniques have been widely used in hepatobiliary surgery, limitations in their application for real-time navigation persist.


The aim of the present study was to evaluate the feasibility and clinical utility of the novel laparoscopic hepatectomy navigation system (LHNS), which fuses preoperative three-dimensional (3D) models with ICG fluorescence imaging to achieve real-time surgical navigation.


We conducted a retrospective review of clinical outcome for 64 patients who underwent laparoscopic hepatectomy from January 2018 to December 2018, including 30 patients who underwent the procedure using the LHNS (LHNS group) and 34 patients who underwent the procedure without LHNS guidance (Non-LHNS group).


There was no significant difference in preoperative characteristics between the two groups. The LHNS group had a significantly less blood loss (285.0 ± 163.0 mL vs. 391.1 ± 242.0 mL; P = 0.047), less intraoperative blood transfusion rate (13.3% vs. 38.2%; P = 0.045), and shorter postoperative hospital stay (7.8 ± 2.1 days vs. 10.6 ± 3.8 days; P < 0.001) than the Non-LHNS group. There was no statistical difference in operative time and the overall complication rate between the two groups. The liver transection line was clearly delineated by the LHNS in 27 patients; however, the projection of boundary was unclear in 2 cases, and in 1 case, the boundary was not clearly displayed by ICG fluorescence imaging.


We developed the LHNS to address limitations of current intraoperative imaging systems. The LHNS is hopefully to become a promising real-time navigation system for laparoscopic hepatectomy.

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  1. 1.

    Buell JF, Cherqui D, Geller DA, O’Rourke N, Iannitti D, Dagher I, Koffron AJ, Thomas M, Gayet B, Han HS, Wakabayashi G, Belli G, Kaneko H, Ker CG, Scatton O, Laurent A, Abdalla EK, Chaudhury P, Dutson E, Gamblin C, D’Angelica M, Nagorney D, Testa G, Labow D, Manas D, Poon RT, Nelson H, Martin R, Clary B, Pinson WC, Martinie J, Vauthey JN, Goldstein R, Roayaie S, Barlet D, Espat J, Abecassis M, Rees M, Fong Y, McMasters KM, Broelsch C, Busuttil R, Belghiti J, Strasberg S, Chari RS (2009) The international position on laparoscopic liver surgery: The Louisville Statement, 2008. Ann Surg 250(5):825–830

    PubMed  Article  Google Scholar 

  2. 2.

    Kawaguchi Y, Fuks D, Kokudo N, Gayet B (2018) Difficulty of laparoscopic liver resection. Ann Surg 267(1):13–17

    PubMed  Article  Google Scholar 

  3. 3.

    Fang C, Tao H, Yang J, Fang Z, Cai W, Liu J, Fan Y (2015) Impact of three-dimensional reconstruction technique in the operation planning of centrally located hepatocellular carcinoma. J Am Coll Surg 220(1):28–37

    PubMed  Article  Google Scholar 

  4. 4.

    Nakayama K, Oshiro Y, Miyamoto R, Kohno K, Fukunaga K, Ohkohchi N (2017) The effect of three-dimensional preoperative simulation on liver surgery. World J Surg 41(7):1840–1847

    PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Mise Y, Hasegawa K, Satou S, Shindoh J, Miki K, Akamatsu N, Arita J, Kaneko J, Sakamoto Y, Kokudo N (2018) How has virtual hepatectomy changed the practice of liver surgery?: experience of 1194 virtual hepatectomy before liver resection and living donor liver transplantation. Ann Surg 268(1):127–133

    PubMed  Article  Google Scholar 

  6. 6.

    Ishizawa T, Fukushima N, Shibahara J, Masuda K, Tamura S, Aoki T, Hasegawa K, Beck Y, Fukayama M, Kokudo N (2009) Real-time identification of liver cancers by using indocyanine green fluorescent imaging. Cancer 115(11):2491–2504

    PubMed  Article  Google Scholar 

  7. 7.

    Baiocchi GL, Diana M, Boni L (2018) Indocyanine green-based fluorescence imaging in visceral and hepatobiliary and pancreatic surgery: State of the art and future directions. World J Gastroenterol 24(27):2921–2930

    PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Koch M, Ntziachristos V (2016) Advancing surgical vision with fluorescence imaging. Annu Rev Med 67:153–164

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Nolden M, Zelzer S, Seitel A, Wald D, Muller M, Franz AM, Maleike D, Fangerau M, Baumhauer M, Maier-Hein L, Maier-Hein KH, Meinzer HP, Wolf I (2013) The medical imaging interaction toolkit: challenges and advances: 10 years of open-source development. Int J Comput Assist Radiol Surg 8(4):607–620

    PubMed  Article  Google Scholar 

  10. 10.

    Mahmoud N, Collins T, Hostettler A, Soler L, Doignon C, Montiel J (2018) Live tracking and dense reconstruction for hand-held monocular endoscopy. IEEE Trans Med Imag 38(1):79–89

    Article  Google Scholar 

  11. 11.

    Yang J, Li H, Campbell D, Jia Y (2016) Go-ICP: a globally optimal solution to 3D ICP point-set registration. IEEE Trans Pattern Anal Mach Intell 38(11):2241–2254

    PubMed  Article  Google Scholar 

  12. 12.

    Mahmoud N, Collins T, Hostettler A, Soler L, Doignon C, Montiel J (2019) Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE Trans Med Imag 38(1):79–89

    Article  Google Scholar 

  13. 13.

    Cherqui D (2015) Laparoscopic liver resection: a new paradigm in the management of hepatocellular carcinoma? J Hepatol 63(3):540–542

    PubMed  Article  Google Scholar 

  14. 14.

    Yoon YI, Kim KH, Kang SH, Kim WJ, Shin MH, Lee SK, Jung DH, Park GC, Ahn CS, Moon DB, Ha TY, Song GW, Hwang S, Lee SG (2017) Pure laparoscopic versus open right hepatectomy for hepatocellular carcinoma in patients with cirrhosis: a propensity score matched analysis. Ann Surg 265(5):856–863

    PubMed  Article  Google Scholar 

  15. 15.

    Moris D, Vernadakis S (2018) Laparoscopic hepatectomy for hepatocellular carcinoma: the opportunities, the challenges, and the limitations. Ann Surg 268(1):e16

    PubMed  Article  Google Scholar 

  16. 16.

    Inoue Y, Arita J, Sakamoto T, Ono Y, Takahashi M, Takahashi Y, Kokudo N, Saiura A (2015) Anatomical liver resections guided by 3-dimensional parenchymal staining using fusion indocyanine green fluorescence imaging. Ann Surg 262(1):105–111

    PubMed  Article  Google Scholar 

  17. 17.

    Conrad C, Fusaglia M, Peterhans M, Lu H, Weber S, Gayet B (2016) Augmented reality navigation surgery facilitates laparoscopic rescue of failed portal vein embolization. J Am Coll Surg 223(4):e31–e34

    PubMed  Article  Google Scholar 

  18. 18.

    Sauer IM, Queisner M, Tang P, Moosburner S, Hoepfner O, Horner R, Lohmann R, Pratschke J (2017) Mixed reality in visceral surgery: development of a suitable workflow and evaluation of intraoperative use-cases. Ann Surg 266(5):706–712

    PubMed  Article  Google Scholar 

  19. 19.

    Marescaux J, Clement JM, Tassetti V, Koehl C, Cotin S, Russier Y, Mutter D, Delingette H, Ayache N (1998) Virtual reality applied to hepatic surgery simulation: the next revolution. Ann Surg 228(5):627–634

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Fang C, Liu J, Fan Y, Yang J, Xiang N, Zeng N (2013) Outcomes of hepatectomy for hepatolithiasis based on 3-dimensional reconstruction technique. J Am Coll Surg 217(2):280–288

    PubMed  Article  Google Scholar 

  21. 21.

    Peterhans M, Vom BA, Dagon B, Inderbitzin D, Baur C, Candinas D, Weber S (2011) A navigation system for open liver surgery: design, workflow and first clinical applications. Int J Med Robot 7(1):7–16

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Tang R, Ma LF, Rong ZX, Li MD, Zeng JP, Wang XD, Liao HE, Dong JH (2018) Augmented reality technology for preoperative planning and intraoperative navigation during hepatobiliary surgery: a review of current methods. Hepatobiliary Pancreat Dis Int 17(2):101–112

    PubMed  Article  Google Scholar 

  23. 23.

    Ishizawa T, Masuda K, Urano Y, Kawaguchi Y, Satou S, Kaneko J, Hasegawa K, Shibahara J, Fukayama M, Tsuji S, Midorikawa Y, Aburatani H, Kokudo N (2014) Mechanistic background and clinical applications of indocyanine green fluorescence imaging of hepatocellular carcinoma. Ann Surg Oncol 21(2):440–448

    PubMed  Article  Google Scholar 

  24. 24.

    Nishino H, Hatano E, Seo S, Nitta T, Saito T, Nakamura M, Hattori K, Takatani M, Fuji H, Taura K, Uemoto S (2018) Real-time navigation for liver surgery using projection mapping with indocyanine green fluorescence: development of the novel medical imaging projection system. Ann Surg 267(6):1134–1140

    PubMed  Article  Google Scholar 

  25. 25.

    Yang T, Liu K, Liu CF, Zhong Q, Zhang J, Yu JJ, Liang L, Li C, Wang MD, Li ZL, Wu H, Xing H, Han J, Lau WY, Zeng YY, Zhou YH, Gu WM, Wang H, Chen TH, Zhang YM, Zhang WG, Pawlik TM, Wu MC, Shen F (2019) Impact of postoperative infective complications on long-term survival after liver resection for hepatocellular carcinoma. Br J Surg 106(9):1228–1236

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Yamamoto J, Kosuge T, Takayama T, Shimada K, Yamasaki S, Ozaki H, Yamaguchi N, Mizuno S, Makuuchi M (1994) Perioperative blood transfusion promotes recurrence of hepatocellular carcinoma after hepatectomy. Surgery 115(3):303–309

    CAS  PubMed  Google Scholar 

  27. 27.

    Katz SC, Shia J, Liau KH, Gonen M, Ruo L, Jarnagin WR, Fong Y, DʼAngelica MI, Blumgart LH, DeMatteo RP (2009) Operative blood loss independently predicts recurrence and survival after resection of hepatocellular carcinoma. Ann Surg 249(4):617–623

    PubMed  Article  Google Scholar 

  28. 28.

    Harada N, Shirabe K, Maeda T, Kayashima H, Ishida T, Maehara Y (2015) Blood transfusion is associated with recurrence of hepatocellular carcinoma after hepatectomy in child–pugh class a patients. World J Surg 39(4):1044–1051

    PubMed  Article  Google Scholar 

  29. 29.

    Kawaguchi Y, Nomi T, Fuks D, Mal F, Kokudo N, Gayet B (2016) Hemorrhage control for laparoscopic hepatectomy: technical details and predictive factors for intraoperative blood loss. Surg Endosc 30(6):2543–2551

    PubMed  Article  Google Scholar 

  30. 30.

    Goh BKP, Chan C, Wong J, Lee S, Lee VTW, Cheow P, Chow PKH, Ooi LLPJ, Chung AYF (2015) Factors associated with and outcomes of open conversion after laparoscopic minor hepatectomy: initial experience at a single institution. Surg Endosc 29(9):2636–2642

    PubMed  Article  Google Scholar 

  31. 31.

    Halls MC, Cipriani F, Berardi G, Barkhatov L, Lainas P, Alzoubi M, D’Hondt M, Rotellar F, Dagher I, Aldrighetti L, Troisi RI, Edwin B, Abu Hilal M (2018) Conversion for unfavorable intraoperative events results in significantly worse outcomes during laparoscopic liver resection. Ann Surg 268(6):1051–1057

    PubMed  Article  Google Scholar 

  32. 32.

    Lim C, Vibert E, Azoulay D, Salloum C, Ishizawa T, Yoshioka R, Mise Y, Sakamoto Y, Aoki T, Sugawara Y (2014) Indocyanine green fluorescence imaging in the surgical management of liver cancers: current facts and future implications. J Visc Surg 151(2):117–124

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Alfano MS, Molfino S, Benedicenti S, Molteni B, Porsio P, Arici E, Gheza F, Botticini M, Portolani N, Baiocchi GL (2019) Intraoperative ICG-based imaging of liver neoplasms: a simple yet powerful tool preliminary results. Surg Endosc 33(1):126–134

    PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Fang C, Zhang P, Qi X (2019) Digital and intelligent liver surgery in the new era: prospects and dilemmas. EbioMedicine 41:693–701

    PubMed  PubMed Central  Article  Google Scholar 

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The authors are grateful for the researchers of the Shenzhen Institute of Advanced Technology of the Chinese Academy of Science, and Professor Liu Lianxin of the First Affiliated Hospital of University of Science and Technology of China for their help in preclinical experiments of LHNS. We also want to thank Wen Sai, the research assistant of Department of Hepatobiliary Surgery in Zhujiang Hospital affiliated to Southern Medical University for her contribution to translation and proofreading of this paper.


This work was supported by the grants from the National Key R&D Program, China (No. 2016YFC0106500), the NSFC-GD Union Foundation, China (No. U1401254), the Major Instrument Project of National Natural Science Fund, China (No. 81627805), and National High Technology Research and Development Program of China (863 program, China) (Nos. 2006AA02Z346 and 2012AA021105).

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Correspondence to Nan Xiang or Fucang Jia or Chihua Fang.

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Peng Zhang, Huoling Luo, Wen Zhu, Jian Yang, Ning Zeng, Yingfang Fan, Sai Wen, Nan Xiang, Fucang Jia, and Chihua Fang have no conflicts of interest or financial ties to disclose.

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Zhang, P., Luo, H., Zhu, W. et al. Real-time navigation for laparoscopic hepatectomy using image fusion of preoperative 3D surgical plan and intraoperative indocyanine green fluorescence imaging. Surg Endosc 34, 3449–3459 (2020).

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  • Laparoscopic hepatectomy
  • Virtual hepatectomy
  • Fluorescence
  • Indocyanine green
  • Surgical navigation