Skip to main content
SpringerLink
Log in

Assessment of DICOM Viewers Capable of Loading Patient-specific 3D Models Obtained by Different Segmentation Platforms in the Operating Room

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

Patient-specific 3D models obtained by the segmentation of volumetric diagnostic images play an increasingly important role in surgical planning. Surgeons use the virtual models reconstructed through segmentation to plan challenging surgeries. Many solutions exist for the different anatomical districts and surgical interventions. The possibility to bring the 3D virtual reconstructions with native radiological images in the operating room is essential for fostering the use of intraoperative planning. To the best of our knowledge, current DICOM viewers are not able to simultaneously connect to the picture archiving and communication system (PACS) and import 3D models generated by external platforms to allow a straight integration in the operating room. A total of 26 DICOM viewers were evaluated: 22 open source and four commercial. Two DICOM viewers can connect to PACS and import segmentations achieved by other applications: Synapse 3D® by Fujifilm and OsiriX by University of Geneva. We developed a software network that converts diffuse visual tool kit (VTK) format 3D model segmentations, obtained by any software platform, to a DICOM format that can be displayed using OsiriX or Synapse 3D. Both OsiriX and Synapse 3D were suitable for our purposes and had comparable performance. Although Synapse 3D loads native images and segmentations faster, the main benefits of OsiriX are its user-friendly loading of elaborated images and it being both free of charge and open source.

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. Udupa JK, Hung HM, Chuang KS: Surface and volume rendering in three-dimensional imaging: a comparison. J Digit Imaging 4:159–168, 1991

    Article  CAS  PubMed  Google Scholar 

  2. Ferrari V, Carbone M, Cappelli C, Boni L, Melfi F, Ferrari M, et al: Value of multidetector computed tomography image segmentation for preoperative planning in general surgery. Surg Endosc 26:616–626, 2012

    Article  PubMed Central  PubMed  Google Scholar 

  3. Haller JW, Banerjee A, Christensen GE, Gado M, Joshi S, Miller MI, et al: Three-dimensional hippocampal MR morphometry with high-dimensional transformation of a neuroanatomic atlas. Radiology 202:504–510, 1997

    Article  CAS  PubMed  Google Scholar 

  4. Goldszal AF, Davatzikos C, Pham DL, Yan MX, Bryan RN, Resnick SM: An image-processing system for qualitative and quantitative volumetric analysis of brain images. J Comput Assist Tomogr 22(5):827–37, 1998

  5. Zhu SC, Yuille A: Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans Pattern Anal Mach Intell 18:884–900, 1996

    Article  Google Scholar 

  6. Caselles V, Kimmel R, Sapiro G: Geodesic active contours. Int J Comput Vis 22:61–79, 1997

    Article  Google Scholar 

  7. Sethian JA, Sethian JA: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, 2nd edition. Cambridge University Press, Cambridge, 1999

    Google Scholar 

  8. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128, 2006

    Article  PubMed  Google Scholar 

  9. Lamata P, Lamata F, Sojar V, Makowski P, Massoptier L, Casciaro S, et al: Use of the Resection Map system as guidance during hepatectomy. Surg Endosc Other Interv Techn 24:2327–2337, 2010

    Article  Google Scholar 

  10. Ferrari V, Cappelli C, Megali G, Pietrabissa A: An anatomy driven approach for generation of 3D models from multi-phase CT images. Int J Comput Assist Radiol Surg, 2008

  11. Cha K, Hadjiiski L, Chan HP, Caoili EM, Cohan RH, Zhou C: CT urography: segmentation of urinary bladder using CLASS with local contour refinement. Phys Med Biol 59:2767–2785, 2014

    Article  PubMed  Google Scholar 

  12. Street E, Hadjiiski L, Sahiner B, Gujar S, Ibrahim M, Mukherji SK, et al: Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation. Med Phys 34:4399–4408, 2007

    Article  PubMed Central  PubMed  Google Scholar 

  13. Cappelli C, Carbone M, Ferrari V, Signori S, De Lio N, Perrone V, Mosca F, Boggi U: Patient-Specific 3D surgical planning to perform cutting edge robotic surgery. The Hamlyn Symposium of Medical Robotics, 2012

  14. Lo Presti G, Ferrari V, Ferrari M, Mosca F, Giannessi F, Ruffoli R: Segmentation procedure for the generation of a 3D model and solid replica of a human skull. Ital J Anat Embryol 17(Supplement 2):99, 2012

    Google Scholar 

  15. Lo Presti G, Carbone M, Cappelli C, Ferrari V, Ferrari M, Caramella D: An integrated platform for an effective liver surgical planning through segmentation of multiphase CT datasets. IJCARS 6(Supplement 1), 2013

  16. Takahashi R: Abdominal Imaging in “SYNAPSE 3D”. FUJIFILM Medical Co., Ltd

  17. Akinari Miyazaki AC, Hiroshi Y, Matsuo N, Nobuhiro T, Hideaki S, Hiroaki S, Hisashi G, Atsushi I, Ikuko M, Tomoko T, Munemasa R, Taro H, Kazuyoshi N, Kentaro S, Taketo Y: 3D images of pancreatic diseases with multi-detector row CT. J Biliary Tract Pancreas 29:1259–1262, 2008

    Google Scholar 

  18. Yukio Oshiro RS, Ryu M, Takeguchi T, Ibukuro K, Ohkohchi N: adiosurgery anatomical investigation of the artery to the caudate lobe of the liver. Japan Res Soc Clin Anat 11:4, 2010

    Google Scholar 

  19. Vides CS, Azpiroz LJ, Jimenez AJ: Plugin for OsiriX: mean shift segmentation. Conf Proc IEEE Eng Med Biol Soc 2007:3060–3, 2007

    PubMed  Google Scholar 

  20. Shimizu A, Kawamura T, Kobatake H: Proposal of computer-aided detection system for three dimensional CT images of liver cancer. Int Congr Ser 1281:1157–1162, 2005. doi:10.1016/j.ics.2005.03.070

  21. Liangjia Z, et al: A complete system for automatic extraction of left ventricular myocardium from CT images using shape segmentation and contour evolution. IEEE Trans Image Process 23(3):1340–1351, 2014

    Article  Google Scholar 

  22. Hammon M, et al: Automated detection and volumetric segmentation of the spleen in CT scans. Röfo 184(8):734–739, 2012

    CAS  PubMed  Google Scholar 

  23. Sboarina A, Foroni RI, Minicozzi A, Antiga L, Lupidi F, Longhi M, et al: Software for hepatic vessel classification: feasibility study for virtual surgery. Int J Comput Assist Radiol Surg 5:39–48, 2010

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work has been financed by “OPERA Project, Funded by Tuscany Region within PAR FAS 2007-2013 Action 1.1 P.I.R. 1.1.B.” and “SILS-Study, Funded by Italian Ministry of Health and Tuscany Region through the call “Ricerca Finalizzata 2009.”

Author information

Authors and Affiliations

  1. EndoCAS Center, Cisanello Hospital, University of Pisa, Via Paradisa 2, 56124, Pisa, Italy

    Giuseppe Lo Presti, Marina Carbone, Mauro Ferrari & Vincenzo Ferrari

  2. Scuola Superiore S’Anna di Studi Universitari e Perfezionamento, Pisa, Italy

    Giuseppe Lo Presti

  3. Faculty of Medicine and Surgery, Università Politecnica delle Marche, Ancona, Italy

    Damiano Ciriaci & Daniele Aramini

  4. Department of Information Engineering, University of Pisa, Pisa, Italy

    Vincenzo Ferrari

Authors
  1. Giuseppe Lo Presti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marina Carbone
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Damiano Ciriaci
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniele Aramini
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mauro Ferrari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Vincenzo Ferrari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giuseppe Lo Presti.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Presti, G.L., Carbone, M., Ciriaci, D. et al. Assessment of DICOM Viewers Capable of Loading Patient-specific 3D Models Obtained by Different Segmentation Platforms in the Operating Room. J Digit Imaging 28, 518–527 (2015). https://doi.org/10.1007/s10278-015-9786-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-015-9786-4

Keywords

Search

Navigation