Skip to main content
SpringerLink
Log in

What are the true volumes of SEGA tumors? Reliability of planimetric and popular semi-automated image segmentation methods

  • Research Article
  • Published:
Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objective

To evaluate the reliability of the standard planimetric methodology of volumetric analysis and three different open-source semi-automated approaches of brain tumor segmentation.

Materials and methods

The volumes of subependymal giant cell astrocytomas (SEGA) examined by 30 MRI studies of 10 patients from a previous everolimus-related trial (EMINENTS study) were estimated using four methods: planimetric method (modified MacDonald ellipsoid method), ITK-Snap (pixel clustering, geodesic active contours, region competition methods), 3D Slicer (level-set thresholding), and NIRFast (k-means clustering, Markov random fields). The methods were compared, and a trial simulation was performed to determine how the choice of approach could influence the final decision about progression or response.

Results

Intraclass correlation coefficient was high (0.95; 95% CI 0.91–0.98). The planimetric method always overestimated the size of the tumor, while virtually no mean difference was found between ITK-Snap and 3D Slicer (P = 0.99). NIRFast underestimated the volume and presented a proportional bias. During the trial simulation, a moderate level of agreement between all the methods (kappa 0.57–0.71, P < 0.002) was noted.

Conclusion

Semi-automated segmentation can ease oncological follow-up but the moderate level of agreement between segmentation methods suggests that the reference standard volumetric method for SEGA tumors should be revised and chosen carefully, as the selection of volumetry tool may influence the conclusion about tumor progression or response.

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

Similar content being viewed by others

References

  1. Jóźwiak S, Nabbout R, Curatolo P (2013) Management of subependymal giant cell astrocytoma (SEGA) associated with tuberous sclerosis complex (TSC): clinical recommendations. Eur J Paediatr Neurol 17:348–352

    Article  PubMed  Google Scholar 

  2. Cappellano AM, Senerchia AA, Adolfo F, Paiva PM, Pinho R, Covic A, Cavalheiro S, Saba N (2013) Successful everolimus therapy for SEGA in pediatric patients with tuberous sclerosis complex. Child’s Nerv Syst 29:2301–2305

    Article  CAS  Google Scholar 

  3. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247

    Article  CAS  PubMed  Google Scholar 

  4. Franz DN, Agricola K, Mays M, Tudor C, Care MM, Holland-Bouley K, Berkowitz N, Miao S, Peyrard S, Krueger DA (2015) Everolimus for subependymal giant cell astrocytoma: 5-year final analysis. Ann Neurol 78:929–938

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber MA, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp Ç, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin HC, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34:1993–2024

    Article  PubMed  Google Scholar 

  6. Gordillo N, Montseny E, Sobrevilla P (2013) State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 31:1426–1438

    Article  PubMed  Google Scholar 

  7. Trelinska J, Dachowska I, Baranska D, Stawiski K, Kotulska K, Fendler W, Jozwiak S, Mlynarski W (2016) Maintenance therapy with everolimus for subependymal giant cell astrocytoma in patients with tuberous sclerosis (the EMINENTS study). Pediatr Blood Cancer 1–7. doi:10.1002/pbc.26347

  8. Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, Verweij J, Van Glabbeke M, van Oosterom AT, Christian MC, Gwyther SG (2000) New guidelines to evaluate the response to treatment in solid tumors. J Natl Cancer Inst 92:205–216

    Article  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  10. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R (2012) 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341

    Article  PubMed  PubMed Central  Google Scholar 

  11. Byrne N, Velasco Forte M, Tandon A, Valverde I, Hussain T (2016) A systematic review of image segmentation methodology, used in the additive manufacture of patient-specific 3D printed models of the cardiovascular system. JRSM Cardiovasc Dis 5:1–9

    CAS  Google Scholar 

  12. Dehghani H, Eames ME, Yalavarthy PK, Davis SC, Srinivasan S, Carpenter CM, Pogue BW, Paulsen KD (2009) Near infrared optical tomography using NIRFAST: algorithm for numerical model and image reconstruction. Commun Numer Methods Eng 25:711–732

    Article  Google Scholar 

  13. Jermyn M, Ghadyani H, Mastanduno MA, Turner W, Davis SC, Dehghani H, Pogue BW (2013) Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography. J Biomed Opt 18:86007

    Article  PubMed  Google Scholar 

  14. Sedgwick P (2013) Limits of agreement (Bland–Altman method). BMJ Br Med J 1630:1–2

    Google Scholar 

  15. Jones M, Dobson A, O’brian S (2011) A graphical method for assessing agreement with the mean between multiple observers using continuous measures. Int J Epidemiol 40:1308–1313

    Article  PubMed  Google Scholar 

  16. Trelinska J, Dachowska I, Kotulska K, Baranska D, Fendler W, Jozwiak S, Mlynarski W (2015) Factors affecting response to everolimus therapy for subependymal giant cell astrocytomas associated with tuberous sclerosis. Pediatr Blood Cancer 62:616–621

    Article  CAS  PubMed  Google Scholar 

  17. Wetzel SG, Johnson G, Tan AGS, Cha S, Knopp EA, Lee VS, Thomasson D, Rofsky NM (2002) Three-dimensional, T1-weighted gradient-echo imaging of the brain with a volumetric interpolated examination. Am J Neuroradiol 23:995–1002

    PubMed  Google Scholar 

  18. Takeda T, Takeda A, Nagaoka T, Kunieda E, Takemasa K, Watanabe M, Hatou T, Oguro S, Katayama M (2008) Gadolinium-enhanced three-dimensional magnetization-prepared rapid gradient-echo (3D MP-RAGE) imaging is superior to spin-echo imaging in delineating brain metastases. Acta Radiol 49:1167–1173

    Article  CAS  PubMed  Google Scholar 

  19. Markl M, Leupold J (2012) Gradient echo imaging. J Magn Reson Imaging 35:1274–1289

    Article  PubMed  Google Scholar 

  20. Cromb A, Saranathan M, Ruet A, Durieux M, De Roquefeuil E, Ouallet JC, Brochet B, Dousset V, Tourdias T (2015) MS lesions are better detected with 3D T1 gradient-echo than with 2D T1 spin-echo gadolinium-enhanced imaging at 3T. Am J Neuroradiol 36:501–507

    Article  Google Scholar 

  21. Weissheimer A, De Menezes LM, Sameshima GT, Enciso R, Pham J, Grauer D (2012) Imaging software accuracy for 3-dimensional analysis of the upper airway. Am J Orthod Dentofac Orthop 142:801–813

    Article  Google Scholar 

  22. Larjani S, Monsalves E, Pebdani H, Krischek B, Gentili F, Cusimano M, Laperriere N, Hayhurst C, Zadeh G (2014) Identifying predictors of early growth response and adverse radiation effects of vestibular schwannomas to radiosurgery. PLoS One 9:e110823

    Article  PubMed  PubMed Central  Google Scholar 

  23. Yuan Q, Rubic M, Seah J, Rae C, Wright IMR, Kaltenbach K, Feller JM, Abdel-Latif ME, Chu C, Oei JL (2014) Do maternal opioids reduce neonatal regional brain volumes? A pilot study. J Perinatol 34:909–913

    Article  CAS  PubMed  Google Scholar 

  24. Liu X, Tuncali K, Wells WM, Morrison PR, Zientara GP (2012) Fully automatic 3D segmentation of iceball for image-guided cryoablation. In: Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS 2012, pp 2327–2330

  25. Follwell MJ, Khu KJ, Cheng L, Xu W, Mikulis DJ, Millar B-A, Tsao MN, Laperriere NJ, Bernstein M, Sahgal A (2012) Volume specific response criteria for brain metastases following salvage stereotactic radiosurgery and associated predictors of response. Acta Oncol (Madr) 51:629–635

    Article  Google Scholar 

  26. Egger J, Kapur T, Fedorov A, Pieper S, Miller JV, Veeraraghavan H, Freisleben B, Golby AJ, Nimsky C, Kikinis R (2013) GBM volumetry using the 3D Slicer medical image computing platform. Sci Rep 3:1364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Dang M, Modi J, Roberts M, Chan C, Mitchell JR (2013) Validation study of a fast, accurate, and precise brain tumor volume measurement. Comput Methods Progr Biomed 111:480–487

    Article  Google Scholar 

  28. Juan-Albarracín J, Fuster-Garcia E, Manjón JV, Robles M, Aparici F, Martí-Bonmatí L, García-Gómez JM (2015) Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One 10:e0125143

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hu T-T, Yan L, Yan P-F, Wang X, Yue G-F (2015) Assessment of the ABC/2 method of epidural hematoma volume measurement as compared to computer-assisted planimetric analysis. Biol Res Nurs 18(1):5–1

    Article  PubMed  Google Scholar 

Download references

Author’s contribution

Stawiski—protocol/project development, data analysis; Trelinska—protocol/project development, data collection or management; Baranska—protocol/project development, data collection or management; Dachowska—data collection or management; Kotulska—data collection or management; Jozwiak—data collection or management, critical review; Fendler—protocol/project development, data analysis, critical review; Mlynarski—data collection or management, critical review.

Author information

Authors and Affiliations

  1. Department of Biostatistics and Translational Medicine, Hematology and Diabetology, Medical University of Lodz, ul. Sporna 36/50, 91-001, Lodz, Poland

    Konrad Stawiski & Wojciech Fendler

  2. Department of Pediatrics, Oncology, Hematology and Diabetology, University Hospital No 4, Lodz, Poland

    Konrad Stawiski, Joanna Trelińska, Iwona Dachowska, Wojciech Fendler & Wojciech Młynarski

  3. Department of Pediatric Radiology, University Hospital No 4, Lodz, Poland

    Dobromiła Baranska

  4. Department of Neurology and Epileptology and Pediatric Rehabilitation, The Children’s Memorial Health Institute, Warsaw, Poland

    Katarzyna Kotulska

  5. Department of Pediatric Neurology, Medical University of Warsaw, Warsaw, Poland

    Sergiusz Jóźwiak

Authors
  1. Konrad Stawiski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joanna Trelińska
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dobromiła Baranska
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Iwona Dachowska
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Katarzyna Kotulska
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sergiusz Jóźwiak
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wojciech Fendler
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wojciech Młynarski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wojciech Fendler.

Ethics declarations

The study was previously approved by the Bioethics Committee of the Medical University of Lodz (# RNN/306/13/KE).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical standard

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.

Funding

The project is financed by the National Science Center grant number 2015/19/B/NZ5/02229.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 176 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stawiski, K., Trelińska, J., Baranska, D. et al. What are the true volumes of SEGA tumors? Reliability of planimetric and popular semi-automated image segmentation methods. Magn Reson Mater Phy 30, 397–405 (2017). https://doi.org/10.1007/s10334-017-0614-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10334-017-0614-3

Keywords

Search

Navigation