Skip to main content
SpringerLink
Log in

Preliminary study for developing a navigation system for gastric cancer surgery using artificial intelligence

  • Original Article
  • Published:
Surgery Today Aims and scope Submit manuscript

Abstract

Purpose

We are attempting to develop a navigation system for safe and effective peripancreatic lymphadenectomy in gastric cancer surgery. As a preliminary study, we examined whether or not the peripancreatic dissection line could be learned by a machine learning model (MLM).

Methods

Among the 41 patients with gastric cancer who underwent radical gastrectomy between April 2019 and January 2020, we selected 6 in whom the pancreatic contour was relatively easy to trace. The pancreatic contour was annotated by a trainer surgeon in 1242 images captured from the video recordings. The MLM was trained using the annotated images from five of the six patients. The pancreatic contour was then segmented by the trained MLM using images from the remaining patient. The same procedure was repeated for all six combinations.

Results

The median maximum intersection over union of each image was 0.708, which was higher than the threshold (0.5). However, the pancreatic contour was misidentified in parts where fatty tissue or thin vessels overlaid the pancreas in some cases.

Conclusion

The contour of the pancreas could be traced relatively well using the trained MLM. Further investigations and training of the system are needed to develop a practical navigation system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA. Jemal a global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

    Article  PubMed  Google Scholar 

  2. Yuya S, Kazuyuki K, Mikito I, Keiji K, Hirofumi S, Sho O, et al. Intraoperative pancreatic injury gives rise to severe postoperative pancreatic fistula: results of a review of unedited videos of the laparoscopic surgical procedures. Int Surg. 2021;105:359–65.

    Article  Google Scholar 

  3. He K, Gkioxari G, Dollar P. Girshick R Mask R-CNN. IEEE Trans Pattern Anal Mach Intell. 2020;42:386–97.

    Article  PubMed  Google Scholar 

  4. Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, et al Microsoft COCO: common objects in context. Lecture Notes Comput Sci. 2014:740–55

  5. Selvaraj MG, Vergara A, Ruiz H, Safari N, Elayabalan S, Ocimati W, et al AI-powered banana diseases and pest detection. Plant Methods. 2019; 15

  6. Madad Zadeh S, Francois T, Calvet L, Chauvet P, Canis M, Bartoli A, et al. SurgAI: deep learning for computerized laparoscopic image understanding in gynaecology. Surg Endosc. 2020;34:5377–89.

    Article  PubMed  Google Scholar 

  7. Caicedo JC, Roth J, Goodman A, Becker T, Karhohs KW, Broisin M, et al. Evaluation of deep learning strategies for nucleus segmentation in fluorescence images. Cytometry A. 2019;95:952–65.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ieiri S, Uemura M, Konishi K, Souzaki R, Nagao Y, Tsutsumi N, et al. Augmented reality navigation system for laparoscopic splenectomy in children based on preoperative CT image using optical tracking device. Pediatr Surg Int. 2012;28:341–6.

    Article  PubMed  Google Scholar 

  9. Kenngott HG, Neuhaus J, Muller-Stich BP, Wolf I, Vetter M, Meinzer HP, et al. Development of a navigation system for minimally invasive esophagectomy. Surg Endosc. 2008;22:1858–65.

    Article  CAS  PubMed  Google Scholar 

  10. Marescaux J, Rubino F, Arenas M, Mutter D. Soler L Augmented-reality-assisted laparoscopic adrenalectomy. JAMA. 2004;292:2214–5.

    CAS  PubMed  Google Scholar 

  11. Okamoto T, Onda S, Matsumoto M, Gocho T, Futagawa Y, Fujioka S, et al. Utility of augmented reality system in hepatobiliary surgery. J Hepatobiliary Pancreat Sci. 2013;20:249–53.

    Article  PubMed  Google Scholar 

  12. Onda S, Okamoto T, Kanehira M, Suzuki F, Ito R, Fujioka S, et al. Identification of inferior pancreaticoduodenal artery during pancreaticoduodenectomy using augmented reality-based navigation system. J Hepatobiliary Pancreat Sci. 2014;21:281–7.

    Article  PubMed  Google Scholar 

  13. Wengert C, Bossard L, Haberling A, Baur C, Szekely G. Cattin PC endoscopic navigation for minimally invasive suturing. Med Image Comput Comput Assist Interv. 2007;10:620–7.

    PubMed  Google Scholar 

  14. Ortmaier T, Groger M, Boehm DH, Falk V. Hirzinger G motion estimation in beating heart surgery. IEEE Trans Biomed Eng. 2005;52:1729–40.

    Article  PubMed  Google Scholar 

  15. Mountney P, Lo B, Thiemjarus S, Stoyanov D. Zhong-Yang G A probabilistic framework for tracking deformable soft tissue in minimally invasive surgery. Med Image Comput Comput Assist Interv. 2007;10:34–41.

    PubMed  Google Scholar 

  16. Mylonas GP, Stoyanov D, Deligianni F, Darzi A. Yang GZ Gaze-contingent soft tissue deformation tracking for minimally invasive robotic surgery. Med Image Comput Comput Assist Interv. 2005;8:843–50.

    PubMed  Google Scholar 

  17. Stoyanov D. Yang GZ Soft tissue deformation tracking for robotic assisted minimally invasive surgery. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:254–7.

    Google Scholar 

Download references

Acknowledgements

We would like to thank Enago (https://www.enago.jp/) for the English language editing.

Author information

Authors and Affiliations

  1. Department of Gastrointestinal Surgery, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8530, Japan

    Yuya Sato, Takatoshi Matsuyama, Keisuke Okuno, Katsumasa Saito, Naoto Fujiwara, Akihiro Hoshino, Kenro Kawada, Masanori Tokunaga & Yusuke Kinugasa

  2. Humanome Lab., Inc., 2-4-10-2F, Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan

    Jun Sese & Masaki Onuki

  3. Artificial Intelligence Research Center, AIST, 2-3-26 Aomi, Koto-ku, Tokyo, 135-0064, Japan

    Jun Sese

  4. Lifematics Inc., BON MARUSAN Bldg. 4F, 3-5 Kanda-Jimbocho, Chiyoda-ku, Tokyo, 101-0051, Japan

    Shogo Mase

Authors
  1. Yuya Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Sese
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takatoshi Matsuyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Masaki Onuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shogo Mase
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Keisuke Okuno
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Katsumasa Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Naoto Fujiwara
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Akihiro Hoshino
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kenro Kawada
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Masanori Tokunaga
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yusuke Kinugasa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masanori Tokunaga.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sato, Y., Sese, J., Matsuyama, T. et al. Preliminary study for developing a navigation system for gastric cancer surgery using artificial intelligence. Surg Today 52, 1753–1758 (2022). https://doi.org/10.1007/s00595-022-02508-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00595-022-02508-5

Keywords

Search

Navigation