Skip to main content

Advertisement

SpringerLink
Log in

Semi-automated vs. manual 3D reconstruction of central mesenteric vascular models: the surgeon’s verdict

  • Published:
Surgical Endoscopy Aims and scope Submit manuscript

Abstract

Background

3D vascular anatomy roadmaps are currently being implemented for surgical planning and navigation. Quality of the reconstruction is critical. The aim of this article is to compare anatomical completeness of models produced by manual and semi-automatic segmentation.

Methods

CT-datasets from patients included in an ongoing trial, underwent 3D vascular reconstruction applying two different segmentation methods. This produced manually-segmented models (MSMs) and semi-automatically segmented models (SAMs) which underwent a paired comparison. Datasets were delivered for reconstruction in 4 batches of 6, of which only batch 4 contained patients with abnormal anatomy. Model completeness was assessed quantitatively using alignment and distance error indexes and qualitatively with systematic inspection. MSMs were the gold standard. Assessed vessels were those of interest to the surgeon performing D3-right colectomy.

Results

24 CT-datasets (13 females, age 44–77) were used in a paired comparative analysis of 48 3D-models. Quantitatively, SAMs showed structural improvement from Batch 1 to 3. Batch 4, with abnormal vessels, showed the highest error-index values. Qualitatively, 91.7% of SAMs did not contain all mesenteric branches relevant to the surgeon. In SAMs, 1 (12.5%) right colic artery-RCA scored as a complete vessel. 3 (37.5%) RCAs scored as incomplete and 4 (50%) RCAs were absent. 6 (25%) of 24 middle colic arteries-MCA scored as complete vessels. 11 (45.8%) scored as incomplete while 7 (29.2%) MCAs were absent. 13 (54.2%) of 24 ileocolic arteries-ICA were complete vessels. 11 (45.8%) scored as incomplete. None (0%) were absent. Additionally, it was observed that 10 (41.7%) of SAMs contained all their jejunal arteries, when compared to MSMs. Calibers of “complete” vessels were significantly higher than in “missing” vessels (MCA p < 0.001, RCA p = 0.016, ICA p < 0.001, JAs p < 0.001).

Conclusion

Despite acceptable results from quantitative analysis, qualitative comparison indicates that semi-automatically generated 3D-models of the central mesenteric vasculature could cause considerable confusion at surgery.

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

Similar content being viewed by others

References

  1. Ferrari V, Carbone M, Cappelli C, Boni L, Melfi F, Ferrari M, Mosca F, Pietrabissa A (2012) Value of multidetector computed tomography image segmentation for preoperative planning in general surgery. Surg Endosc 26:616–626

    Article  Google Scholar 

  2. Luzon JA, Andersen BT, Stimec BV, Fasel JHD, Bakka AO, Kazaryan AM, Ignjatovic D (2019) Implementation of 3D printed superior mesenteric vascular models for surgical planning and/or navigation in right colectomy with extended D3 mesenterectomy: comparison of virtual and physical models to the anatomy found at surgery. Surg Endosc 33:567–575

    Article  Google Scholar 

  3. Marconi S, Pugliese L, Botti M, Peri A, Cavazzi E, Latteri S, Auricchio F, Pietrabissa A (2017) Value of 3D printing for the comprehension of surgical anatomy. Surg Endosc 31:4102–4110

    Article  Google Scholar 

  4. Nesgaard JM, Stimec BV, Bakka AO, Edwin B, Ignjatovic D (2015) Navigating the mesentery: a comparative pre- and per-operative visualization of the vascular anatomy. Colorectal Dis 17:810–818

    Article  CAS  Google Scholar 

  5. Luccichenti G, Cademartiri F, Pezzella FR, Runza G, Belgrano M, Midiri M, Sabatini U, Bastianello S, Krestin GP (2005) 3D reconstruction techniques made easy: know-how and pictures. Eur Radiol 15:2146–2156

    Article  Google Scholar 

  6. van Ginneken B, Heimann T, Styner M (2007) 3D segmentation in the clinic: a grand challenge. Springer, Berlin

    Google Scholar 

  7. Lesage D, Angelini ED, Bloch I, Funka-Lea G (2009) A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med Image Anal 13:819–845

    Article  Google Scholar 

  8. Garcia-Granero A, Sanchez-Guillen L, Fletcher-Sanfeliu D, Flor-Lorente B, Frasson M, Sancho Muriel J, Alvarez Serrado E, Pellino G, Grifo Albalat I, Giner F, Roca Estelles MJ, Esclapez Valero P, Garcia-Granero E (2018) Application of three-dimensional printing in laparoscopic dissection to facilitate D3-lymphadenectomy for right colon cancer. Tech Coloproctol 22:129–133

    Article  CAS  Google Scholar 

  9. Nesgaard JM, Stimec BV, Bakka AO, Edwin B, Ignjatovic D, the RCCsg (2017) Navigating the mesentery: part II. Vascular abnormalities and a review of the literature. Colorectal Dis 19:656–666

    Article  CAS  Google Scholar 

  10. Spasojevic M, Stimec BV, Fasel JF, Terraz S, Ignjatovic D (2011) 3D relations between right colon arteries and the superior mesenteric vein: a preliminary study with multidetector computed tomography. Surg Endosc 25:1883–1886

    Article  CAS  Google Scholar 

  11. Kumar RP, Albregtsen F, Reimers M, Edwin B, Lango T, Elle OJ (2015) Three-dimensional blood vessel segmentation and centerline extraction based on two-dimensional cross-section analysis. Ann Biomed Eng 43:1223–1234

    Article  Google Scholar 

  12. Yushkevich PA, Gao Y, Gerig G (2016) ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 3342–3345

  13. Kumar RP (2014) Fast blood vessel segmentation for surgical and interventional planning and navigation. PhD Thesis, University of Oslo, Norway

  14. Kumar R, Barkhatov L, Edwin B, Albregtsen F, Elle O (2018) Portal and hepatic vein segmentation with leak restriction: a pilot study. Springer, EMBEC 2017, NBC 2017

  15. Lavoué G, Corsini M (2010) A comparison of perceptually-based metrics for objective evaluation of geometry processing. IEEE Trans Multimedia 12:636–649

    Article  Google Scholar 

  16. Li XR, Zhao Z (2001) Measures of performance for evaluation of estimators and filters. In: SPIE

  17. Witowski J, Wake N, Grochowska A, Sun Z, Budzyński A, Major P, Popiela TJ, Pędziwiatr M (2019) Investigating accuracy of 3D printed liver models with computed tomography. Quant Imaging Med Surg 9:43–52

    Article  Google Scholar 

  18. Murono K, Kawai K, Ishihara S, Otani K, Yasuda K, Nishikawa T, Tanaka T, Kiyomatsu T, Hata K, Nozawa H, Yamaguchi H, Watanabe T (2016) Evaluation of the vascular anatomy of the right-sided colon using three-dimensional computed tomography angiography: a single-center study of 536 patients and a review of the literature. Int J Colorectal Dis 31:1633–1638

    Article  Google Scholar 

  19. Flor N, Campari A, Ravelli A, Lombardi MA, Pisani Ceretti A, Maroni N, Opocher E, Cornalba G (2015) Vascular map combined with CT colonography for evaluating candidates for laparoscopic colorectal surgery. Korean J Radiol 16:821–826

    Article  Google Scholar 

  20. Mari FS, Nigri G, Pancaldi A, De Cecco CN, Gasparrini M, Dall’Oglio A, Pindozzi F, Laghi A, Brescia A (2013) Role of CT angiography with three-dimensional reconstruction of mesenteric vessels in laparoscopic colorectal resections: a randomized controlled trial. Surg Endosc 27:2058–2067

    Article  Google Scholar 

  21. Miao R-C, Wan Y, Zhang X-G, Zhang X, Deng Y, Liu C (2018) Devascularization of the superior mesenteric vein without reconstruction during surgery for retroperitoneal liposarcoma: a case report and review of literature. World J Gastroenterol 24:2406–2412

    Article  Google Scholar 

  22. Mori K, Oda M, Egusa T, Jiang Z, Kitasaka T, Fujiwara M, Misawa K (2010) Automated nomenclature of upper abdominal arteries for displaying anatomical names on virtual laparoscopic images. Springer, Berlin, Heidelberg, pp 353–362

    Google Scholar 

  23. Turmezei TD, Cockburn JF (2009) Digital subtraction angiography of the superior mesenteric artery: identifying arterial branches. Clin Anat 22:777–779

    Article  CAS  Google Scholar 

  24. Zhang W, Liu J, Yao J, Louie A, Nguyen TB, Wank S, Nowinski WL, Summers RM (2013) Mesenteric vasculature-guided small bowel segmentation on 3-D CT. IEEE Trans Med Imaging 32:2006–2021

    Article  Google Scholar 

  25. Nesgaard JM, Stimec BV, Ignjatovic D (2017) Is tracing vessels to the origin in right colectomy really impossible? Dis Colon Rectum 60:e607–e608

    Article  Google Scholar 

  26. Coffey JC, O’Leary DP (2016) The mesentery: structure, function, and role in disease. Lancet Gastroenterol Hepatol 1:238–247

    Article  Google Scholar 

  27. Guyton AC, Hall JE (2011) Guyton & Hall physiology review, 2nd edn. Elsevier Saunders, Philadelphia

    Google Scholar 

  28. Willard CD, Kjaestad E, Stimec BV, Edwin B, Ignjatovic D (2019) Preoperative anatomical road mapping reduces variability of operating time, estimated blood loss, and lymph node yield in right colectomy with extended D3 mesenterectomy for cancer. Int J Colorectal Dis 34:151–160

    Article  Google Scholar 

  29. Gaupset R, Nesgaard JM, Kazaryan AM, Stimec BV, Edwin B, Ignjatovic D (2018) Introducing anatomically Correct CT-guided laparoscopic right colectomy with D3 anterior posterior extended mesenterectomy: initial experience and technical pitfalls. J Laparoendosc Adv Surg Tech Part A 28:1174–1182

    Article  Google Scholar 

  30. Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15:29

    Article  Google Scholar 

  31. Celi LA, Fine B, Stone DJ (2019) An awakening in medicine: the partnership of humanity and intelligent machines. Lancet Digit Health 1:e255–e257

    Article  Google Scholar 

Download references

Acknowledgements

We thank Dr. Rafael Palomar for his kind support in the quantitative methodology of this paper.

Author information

Authors and Affiliations

  1. Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

    Javier A. Luzon, Arne O. Bakka, Bjørn Edwin & Dejan Ignjatovic

  2. Division of Surgery, Department of Digestive Surgery, Akershus University Hospital, Lørenskog, Norway

    Javier A. Luzon, Arne O. Bakka & Dejan Ignjatovic

  3. The Intervention Centre, Oslo University Hospital, Oslo, Norway

    Rahul P. Kumar, Ole Jakob Elle & Bjørn Edwin

  4. Faculty of Medicine, Teaching Unit, Anatomy Sector, University of Geneva, Geneva, Switzerland

    Bojan V. Stimec

  5. Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway

    Ole Jakob Elle

  6. Department of Hepatopancreatobiliary Surgery, Oslo University Hospital-Rikshospitalet, Oslo, Norway

    Bjørn Edwin

Authors
  1. Javier A. Luzon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rahul P. Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bojan V. Stimec
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ole Jakob Elle
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arne O. Bakka
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bjørn Edwin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dejan Ignjatovic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Javier A. Luzon.

Ethics declarations

Conflict of interest

Drs. Javier A. Luzon, Rahul P. Kumar, Bojan V. Stimec, Ole Jakob Elle, Arne O. Bakka, Bjørn Edwin, and Dejan Ignjatovic have no conflicts of interest of financial ties to disclose.

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

Luzon, J.A., Kumar, R.P., Stimec, B.V. et al. Semi-automated vs. manual 3D reconstruction of central mesenteric vascular models: the surgeon’s verdict. Surg Endosc 34, 4890–4900 (2020). https://doi.org/10.1007/s00464-019-07275-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00464-019-07275-y

Keywords

Search

Navigation