Skip to main content

Advertisement

SpringerLink
Log in

Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study

  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To compare the repeatability, agreement, and efficiency of MRI- and CT-based semiautomated liver segmentation for the assessment of total and subsegmental liver volume.

Methods

This retrospective study was conducted in 31 subjects who underwent contemporaneous liver MRI and CT. Total and subsegmental liver volumes were segmented from contrast-enhanced 3D gradient-recalled echo MRI sequences and CT images. Semiautomated segmentation was based on variational interpolation and Laplacian mesh optimization. All segmentations were repeated after 2 weeks. Manual segmentation of CT images using an active contour tool was used as the reference standard. Repeatability and agreement of the methods were evaluated with intra-class correlation coefficients (ICC) and Bland–Altman analysis. Total interaction time was recorded.

Results

Intra-reader ICC were ≥0.987 for MRI and ≥0.995 for CT. Intra-reader repeatability was 30 ± 217 ml (bias ± 1.96 SD) (95% limits of agreement: −187 to 247 ml) for MRI and −10 ± 143 ml (−153 to 133 ml) for CT. Inter-method ICC between semiautomated and manual volumetry were ≥0.995 for MRI and ≥0.986 for CT. Inter-method segmental ICC varied between 0.584 and 0.865 for MRI and between 0.596 and 0.890 for CT. Inter-method agreement was –14 ± 136 ml (−150 to 122 ml) for MRI and 50 ± 226 ml (−176 to 276 ml) for CT. Inter-method segmental agreement ranged from 10 ± 47 ml (−37 to 57 ml) to 2 ± 214 ml (−212 to 216 ml) for MRI and 9 ± 45 ml (−36 to 54 ml) to −46 ± 183 ml (−229 to 137 ml) for CT. Interaction time (mean ± SD) was significantly shorter for MRI-based semiautomated segmentation (7.2 ± 0.1 min, p < 0.001) and for CT-based semiautomated segmentation (6.5 ± 0.2 min, p < 0.001) than for CT-based manual segmentation (14.5 ± 0.4 min).

Conclusion

MRI-based semiautomated segmentation provides similar repeatability and agreement to CT-based segmentation for total liver volume.

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
Fig. 6

Similar content being viewed by others

REFERENCES

  1. Ferrero A, Vigano L, Polastri R, et al. (2007) Postoperative liver dysfunction and future remnant liver: where is the limit? Results of a prospective study. World J Surg 31(8):1643–1651. doi:10.1007/s00268-007-9123-2

    Article  PubMed  Google Scholar 

  2. Hermoye L, Laamari-Azjal I, Cao Z, et al. (2005) Liver segmentation in living liver transplant donors: comparison of semiautomatic and manual methods. Radiology 234(1):171–178. doi:10.1148/radiol.2341031801

    Article  PubMed  Google Scholar 

  3. Massoptier L, Casciaro S (2008) A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans. Eur Radiol 18(8):1658–1665

    Article  PubMed  Google Scholar 

  4. Campadelli P, Casiraghi E, Esposito A (2009) Liver segmentation from computed tomography scans: a survey and a new algorithm. Artif Intell Med 45(2–3):185–196. doi:10.1016/j.artmed.2008.07.020

    Article  PubMed  Google Scholar 

  5. Masutani Y, Uozumi K, Akahane M, Ohtomo K (2006) Liver CT image processing: a short introduction of the technical elements. Eur J Radiol 58(2):246–251

    Article  CAS  PubMed  Google Scholar 

  6. Nakayama Y, Li Q, Katsuragawa S, et al. (2006) Automated hepatic volumetry for living related liver transplantation at multisection CT. Radiology 240(3):743–748. doi:10.1148/radiol.2403050850

    Article  PubMed  Google Scholar 

  7. Fulcher AS, Szucs RA, Bassignani MJ, Marcos A (2001) Right lobe living donor liver transplantation: preoperative evaluation of the donor with MR imaging. Ajr 176(6):1483–1491

    Article  CAS  PubMed  Google Scholar 

  8. Reeder SB, Cruite I, Hamilton G, Sirlin CB (2011) Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy. J Magn Reson Imaging JMRI 34 (4):spcone. doi:10.1002/jmri.22775

  9. Hernando D, Levin YS, Sirlin CB, Reeder SB (2014) Quantification of liver iron with MRI: state of the art and remaining challenges. J Magn Reson Imaging 40(5):1003–1021. doi:10.1002/jmri.24584

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tang A, Chen J, Le TA, et al. (2015) Cross-sectional and longitudinal evaluation of liver volume and total liver fat burden in adults with nonalcoholic steatohepatitis. Abdom Imaging 40(1):26–37. doi:10.1007/s00261-014-0175-0

    Article  PubMed  PubMed Central  Google Scholar 

  11. d’Assignies G, Kauffmann C, Boulanger Y, et al. (2011) Simultaneous assessment of liver volume and whole liver fat content: a step towards one-stop shop preoperative MRI protocol. Eur Radiol 21(2):301–309. doi:10.1007/s00330-010-1941-1

    Article  PubMed  Google Scholar 

  12. Le TA, Chen J, Changchien C, et al. (2012) Effect of colesevelam on liver fat quantified by magnetic resonance in nonalcoholic steatohepatitis: a randomized controlled trial. Hepatology 56(3):922–932. doi:10.1002/hep.25731

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Brenner DJ, Hall EJ (2007) Computed tomography–an increasing source of radiation exposure. N Engl J Med 357(22):2277–2284. doi:10.1056/NEJMra072149

    Article  CAS  PubMed  Google Scholar 

  14. Huynh HT, Karademir I, Oto A, Suzuki K (2014) Computerized liver volumetry on MRI by using 3D geodesic active contour segmentation. Ajr 202(1):152–159. doi:10.2214/AJR.13.10812

    Article  PubMed  PubMed Central  Google Scholar 

  15. Heimann T, van Ginneken B, Styner MA, et al. (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251–1265. doi:10.1109/TMI.2009.2013851

    Article  PubMed  Google Scholar 

  16. Udupa JK, Leblanc VR, Zhuge Y, et al. (2006) A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph 30(2):75–87. doi:10.1016/j.compmedimag.2005.12.001

    Article  PubMed  Google Scholar 

  17. Chartrand G, Cresson T, Chav R, et al. (2013) Semi-automated liver CT segmentation using Laplacian meshes. In: 2014 IEEE international symposium on biomedical imaging, Beiing, China

  18. Suzuki K, Huynh HT, Liu Y, et al. (2013) Computerized segmentation of liver in hepatic CT and MRI by means of level-set geodesic active contouring. Conf Proc IEEE Eng Med Biol Soc 2013:2984–2987. doi:10.1109/EMBC.2013.6610167

    PubMed  PubMed Central  Google Scholar 

  19. Couinaud C (1957) Le Foie: études anatomiques et chirurgicales. Paris: Masson

    Google Scholar 

  20. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active Contour Models. Int J Comput Vis 1(4):321–331

    Article  Google Scholar 

  21. Gotra A, Chartrand G, Massicotte-Tisluck K, et al. (2015) Validation of a semiautomated liver segmentation method using CT for accurate volumetry. Academic radiology. doi:10.1016/j.acra.2015.03.010

    PubMed  Google Scholar 

  22. Wimmer A, Soza G, Hornegger J (2007) Two-stage semi-automatic organ segmentation framework using radial basis functions and level sets. In: Proceedings of MICCAI 2007 workshop: 3D segmentation in the clinic-a grand challenge, pp. 179–188

  23. Turk G, O’Brien J (2002) Modelling with Implicit Surfaces that Interpolate. ACM Trans Graph 21(4):855–873. doi:10.1145/571647.571650

    Article  Google Scholar 

  24. Heckel F, Konrad O, Hahn HK, Peitgen HO (2010) Interactive 3D medical image segmentation with energy-minimizing implicit functions. Comput Graph 35(2):275–287

    Article  Google Scholar 

  25. Nealen A, Igarashi T, Sorkine O, Alexa M (2006) Laplacian mesh optimization. In: Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia - GRAPHITE ‘06. doi:10.1145/1174429.1174494

  26. Jm B, Dg A (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327(8476):307–310. doi:10.1016/S0140-6736(86)90837-8

    Article  Google Scholar 

  27. Mazonakis M, Damilakis J, Maris T, Prassopoulos P, Gourtsoyiannis N (2002) Comparison of two volumetric techniques for estimating liver volume using magnetic resonance imaging. J Magn Reson Imaging JMRI 15(5):557–563. doi:10.1002/jmri.10109

    Article  PubMed  Google Scholar 

  28. Farraher SW, Jara H, Chang KJ, Hou A, Soto JA (2005) Liver and spleen volumetry with quantitative MR imaging and dual-space clustering segmentation. Radiology 237(1):322–328. doi:10.1148/radiol.2371041416

    Article  PubMed  Google Scholar 

  29. Torkzad MR, Noren A, Kullberg J (2012) Stereology: a novel technique for rapid assessment of liver volume. Insights Imaging 3(4):387–393. doi:10.1007/s13244-012-0166-z

    Article  PubMed  PubMed Central  Google Scholar 

  30. Suzuki K, Epstein ML, Kohlbrenner R, et al. (2011) Quantitative radiology: automated CT liver volumetry compared with interactive volumetry and manual volumetry. Ajr 197(4):W706–W712. doi:10.2214/AJR.10.5958

    Article  PubMed  PubMed Central  Google Scholar 

  31. Luciani A, Rusko L, Baranes L, et al. (2012) Automated liver volumetry in orthotopic liver transplantation using multiphase acquisitions on MDCT. Ajr 198(6):W568–W574. doi:10.2214/AJR.11.7468

    Article  PubMed  Google Scholar 

  32. Lemke AJ, Brinkmann MJ, Pascher A, et al. (2003) Accuracy of the CT-estimated weight of the right hepatic lobe prior to living related liver donation (LRLD) for predicting the intraoperatively measured weight of the graft. RoFo Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin 175(9):1232–1238. doi:10.1055/s-2003-41938

    Article  PubMed  Google Scholar 

  33. Frericks BB, Kiene T, Stamm G, Shin H, Galanski M (2004) CT-based liver volumetry in a porcine model: impact on clinical volumetry prior to living donated liver transplantation. RoFo Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin 176(2):252–257. doi:10.1055/s-2004-817636

    Article  CAS  PubMed  Google Scholar 

  34. Niehues SM, Unger JK, Malinowski M, et al. (2010) Liver volume measurement: reason of the difference between in vivo CT-volumetry and intraoperative ex vivo determination and how to cope it. Eur J Med Res 15(8):345–350

    Article  PubMed  PubMed Central  Google Scholar 

  35. Hiroshige S, Shimada M, Harada N, et al. (2003) Accurate preoperative estimation of liver-graft volumetry using three-dimensional computed tomography. Transplantation 75(9):1561–1564. doi:10.1097/01.TP.0000053755.08825.12

    Article  PubMed  Google Scholar 

  36. Schiano TD, Bodian C, Schwartz ME, Glajchen N, Min AD (2000) Accuracy and significance of computed tomographic scan assessment of hepatic volume in patients undergoing liver transplantation. Transplantation 69(4):545–550

    Article  CAS  PubMed  Google Scholar 

  37. Gotra A, Chartrand G, Massicotte-Tisluck K, et al. (2015) Validation of a semiautomated liver segmentation method using CT for accurate volumetry. Acad Radiol 22(9):1088–1098. doi:10.1016/j.acra.2015.03.010

    Article  PubMed  Google Scholar 

  38. Heimann T, Wolf I, Meinzer HP (2006) Active shape models for a fully automated 3D segmentation of the liver–an evaluation on clinical data. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 9(Pt 2):41–48

    Google Scholar 

  39. Rusko L, Bekes G (2011) Liver segmentation for contrast-enhanced MR images using partitioned probabilistic model. Int J Comput Assist Radiol Surg 6(1):13–20. doi:10.1007/s11548-010-0493-9

    Article  PubMed  Google Scholar 

  40. Lee J, Kim N, Lee H et al. (2007) Efficient liver segmentation exploiting level-set speed images with 2.5D shape propagation. In: Proceedings of MICCAI workshop on 3-D segmentatation in the clinic: a grand challenge, pp. 189–196.

  41. Gloger O, Kuhn J, Stanski A, Volzke H, Puls R (2010) A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images. Magn Reson Imaging 28(6):882–897. doi:10.1016/j.mri.2010.03.010

    Article  PubMed  Google Scholar 

  42. Sahin B, Ergur H (2006) Assessment of the optimum section thickness for the estimation of liver volume using magnetic resonance images: a stereological gold standard study. Eur J Radiol 57(1):96–101. doi:10.1016/j.ejrad.2005.07.006

    Article  PubMed  Google Scholar 

  43. Lemke AJ, Brinkmann MJ, Schott T, et al. (2006) Living donor right liver lobes: preoperative CT volumetric measurement for calculation of intraoperative weight and volume. Radiology 240(3):736–742

    Article  PubMed  Google Scholar 

  44. Yamanaka J, Saito S, Fujimoto J (2007) Impact of preoperative planning using virtual segmental volumetry on liver resection for hepatocellular carcinoma. World J Surg 31(6):1249–1255. doi:10.1007/s00268-007-9020-8

    Article  PubMed  Google Scholar 

  45. Reiner CS, Karlo C, Petrowsky H, et al. (2009) Preoperative liver volumetry: how does the slice thickness influence the multidetector computed tomography- and magnetic resonance-liver volume measurements? J Comput Assist Tomogr 33(3):390–397. doi:10.1097/RCT.0b013e3181806c29

    Article  PubMed  Google Scholar 

  46. Karlo C, Reiner CS, Stolzmann P, et al. (2010) CT- and MRI-based volumetry of resected liver specimen: comparison to intraoperative volume and weight measurements and calculation of conversion factors. Eur J Radiol 75(1):e107–e111

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This study was funded by 1) an operating Grant from the Canadian Institute of Health Research Institute of Nutrition, Metabolism and Diabetes (CIHR-INMD #273738), a Seed Grant from the Quebec Bio-Imaging Network (QBIN #8436-0501), a New Researcher Start-up Grant from the Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), and a Career Award from the Fonds de recherche du Québec en Santé (FRQS-ARQ #26993) to An Tang; 2) a MITACS industrial research award (IT02111) to Gabriel Chartrand; and 3) a Research Chair of Canada in 3D Imaging and Biomedical engineering award to Jacques de Guise.

Author information

Authors and Affiliations

  1. Department of Radiology, Radio-oncology and Nuclear Medicine, University of Montreal, Saint-Luc Hospital, 1058 Rue Saint-Denis, Montreal, QC, H2X 3J4, Canada

    Akshat Gotra, Kim-Nhien Vu, Karine Massicotte-Tisluck & An Tang

  2. Department of Radiology, McGill University, Montreal General Hospital, 1650 Cedar Avenue, Montreal, QC, H3G 1A4, Canada

    Akshat Gotra

  3. Imaging and Orthopaedics Research Laboratory (LIO), École de Technologie Supérieure, Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), 900 Rue Saint-Denis, Montreal, QC, H2X 0A9, Canada

    Gabriel Chartrand & Jacques A. de Guise

  4. Department of Hepato-biliary and Pancreatic Surgery, University of Montreal, Saint-Luc Hospital, 1058 Rue Saint-Denis, Montreal, QC, H2X 3J4, Canada

    Franck Vandenbroucke-Menu

  5. Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), 900 Rue Saint-Denis, Montreal, QC, H2X 0A9, Canada

    An Tang

Authors
  1. Akshat Gotra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gabriel Chartrand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kim-Nhien Vu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Franck Vandenbroucke-Menu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Karine Massicotte-Tisluck
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jacques A. de Guise
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. An Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to An Tang.

Ethics declarations

Conflict of interest

Akshat Gotra, Gabriel Chartrand, Kim-Nhien Vu, Franck Vandenbroucke-Menu, Karine Massicotte-Tisluck, Jacques A. de Guise, and An Tang declares that they have no conflict of interest.

Disclosures

All the other authors have no disclosures of possible conflicts of interest and/or commercial involvement.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

This study received approval prior to commencement from our institutional review board. Given the study design (retrospective, cross-sectional), informed consent requirements were waived.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gotra, A., Chartrand, G., Vu, KN. et al. Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study. Abdom Radiol 42, 478–489 (2017). https://doi.org/10.1007/s00261-016-0912-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-016-0912-7

Keywords

Search

Navigation