Skip to main content

Advertisement

SpringerLink
Log in

A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study

  • Diagnostic Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation.

Methods

Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis.

Results

With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (ρ c) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement.

Conclusions

NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP.

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. Bauer S, Wiest R, Nolte LP, Reyes M (2013) A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol 58(13):R97–129. doi:10.1088/0031-9155/58/13/R97

    Article  PubMed  Google Scholar 

  2. Prastawa M, Bullitt E, Moon N, Van Leemput K, Gerig G (2003) Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad Radiol 10(12):1341–1348

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chen T, Guo D, Fang Z, Zhong W, Zhao J, Jiang Y (2014) Preliminary study of whole-brain CT perfusion imaging in patients with intracranial tumours adjacent to large blood vessels. Clin Radiol 69(1):e25–e32. doi:10.1016/j.crad.2013.08.010

    Article  CAS  PubMed  Google Scholar 

  4. Coolens C, Driscoll B, Chung C, Shek T, Gorjizadeh A, Menard C, Jaffray D (2015) Automated voxel-based analysis of volumetric dynamic contrast-enhanced CT data improves measurement of serial changes in tumor vascular biomarkers. Int J Radiat Oncol Biol Phys 91(1):48–57. doi:10.1016/j.ijrobp.2014.09.028

    Article  PubMed  Google Scholar 

  5. Hiwatashi A, Togao O, Yamashita K, Kikuchi K, Yoshimoto K, Mizoguchi M, Suzuki SO, Yoshiura T, Honda H (2016) Evaluation of glioblastomas and lymphomas with whole-brain CT perfusion: comparison between a delay-invariant singular-value decomposition algorithm and a Patlak plot. Journal of neuroradiology Journal de neuroradiologie. doi:10.1016/j.neurad.2016.01.147

    PubMed  Google Scholar 

  6. Shankar JJ, Lum C (2011) Whole brain CT perfusion on a 320-slice CT scanner. Indian J Radiol Imaging 21(3):209–214. doi:10.4103/0971-3026.85370

    Article  PubMed  PubMed Central  Google Scholar 

  7. Xyda A, Haberland U, Klotz E, Jung K, Bock HC, Schramm R, Knauth M, Schramm P (2012) Diagnostic performance of whole brain volume perfusion CT in intra-axial brain tumors: preoperative classification accuracy and histopathologic correlation. Eur J Radiol 81(12):4105–4111. doi:10.1016/j.ejrad.2012.08.005

    Article  PubMed  Google Scholar 

  8. Miles KA (1991) Measurement of tissue perfusion by dynamic computed tomography. Br J Radiol 64(761):409–412. doi:10.1259/0007-1285-64-761-409

    Article  CAS  PubMed  Google Scholar 

  9. Patlak CS, Blasberg RG (1985) Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 5(4):584–590. doi:10.1038/jcbfm.1985.87

    Article  CAS  Google Scholar 

  10. Neil Birkbeck DC, Martin Jagersand, Albert Murtha, Tibor Kesztyues (2009) An interactive graph cut method for brain tumor segmentation. Workshop on Applications of Computer Vision (WACV), pp 1–7. doi:10.1109/WACV.2009.5403049

  11. Boykov Y, Funka-Lea G (2006) Graph cuts and efficient N-D image segmentation. Int J Comput Vis 70(2):109–131. doi:10.1007/s11263-006-7934-5

    Article  Google Scholar 

  12. Essig M, Shiroishi MS, Nguyen TB, Saake M, Provenzale JM, Enterline D, Anzalone N, Dorfler A, Rovira A, Wintermark M, Law M (2013) Perfusion MRI: the five most frequently asked technical questions. AJR Am J Roentgenol 200(1):24–34. doi:10.2214/ajr.12.9543

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wintermark M, Sesay M, Barbier E, Borbely K, Dillon WP, Eastwood JD, Glenn TC, Grandin CB, Pedraza S, Soustiel JF, Nariai T, Zaharchuk G, Caille JM, Dousset V, Yonas H (2005) Comparative overview of brain perfusion imaging techniques. Stroke; a journal of cerebral circulation 36(9):e83–e99. doi:10.1161/01.STR.0000177884.72657.8b

    Article  Google Scholar 

  14. Geer CP, Simonds J, Anvery A, Chen MY, Burdette JH, Zapadka ME, Ellis TL, Tatter SB, Lesser GJ, Chan MD, McMullen KP, Johnson AJ (2012) Does MR perfusion imaging impact management decisions for patients with brain tumors? A prospective study. AJNR Am J Neuroradiol 33(3):556–562. doi:10.3174/ajnr.A2811

    Article  CAS  PubMed  Google Scholar 

  15. Korfiatis P, Erickson B (2014) The basics of diffusion and perfusion imaging in brain tumors. Appl Radiol 43(7):22–29

    PubMed  PubMed Central  Google Scholar 

  16. Bisdas S, Medov L, Baghi M, Konstantinou GN, Wagenblast J, Thng CH, Vogl TJ, Koh TS (2008) A comparison of tumour perfusion assessed by deconvolution-based analysis of dynamic contrast-enhanced CT and MR imaging in patients with squamous cell carcinoma of the upper aerodigestive tract. Eur Radiol 18(4):843–850. doi:10.1007/s00330-007-0827-3

    Article  PubMed  Google Scholar 

  17. Jain R (2011) Perfusion CT imaging of brain tumors: an overview. AJNR Am J Neuroradiol 32(9):1570–1577. doi:10.3174/ajnr.A2263

    Article  CAS  PubMed  Google Scholar 

  18. Pillai JJ (2013) Functional brain tumor imaging. Springer, New York

    Google Scholar 

  19. Konstas AA, Goldmakher GV, Lee TY, Lev MH (2009) Theoretic basis and technical implementations of CT perfusion in acute ischemic stroke, part 2: technical implementations. AJNR Am J Neuroradiol 30(5):885–892. doi:10.3174/ajnr.A1492

    Article  CAS  PubMed  Google Scholar 

  20. Mazzara GP, Velthuizen RP, Pearlman JL, Greenberg HM, Wagner H (2004) Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. Int J Radiat Oncol Biol Phys 59(1):300–312. doi:10.1016/j.ijrobp.2004.01.026

    Article  PubMed  Google Scholar 

  21. Hakyemez B, Erdogan C, Bolca N, Yildirim N, Gokalp G, Parlak M (2006) Evaluation of different cerebral mass lesions by perfusion-weighted MR imaging. Journal of magnetic resonance imaging : JMRI 24(4):817–824. doi:10.1002/jmri.20707

    Article  PubMed  Google Scholar 

  22. Zimny A, Sasiadek M (2011) Contribution of perfusion-weighted magnetic resonance imaging in the differentiation of meningiomas and other extra-axial tumors: case reports and literature review. J Neuro-Oncol 103(3):777–783. doi:10.1007/s11060-010-0445-9

    Article  Google Scholar 

  23. Larke FJ, Kruger RL, Cagnon CH, Flynn MJ, McNitt-Gray MM, Wu X, Judy PF, Cody DD (2011) Estimated radiation dose associated with low-dose chest CT of average-size participants in the National Lung Screening Trial. AJR Am J Roentgenol 197(5):1165–1169. doi:10.2214/ajr.11.6533

    Article  PubMed  Google Scholar 

  24. Cho SK, Na DG, Ryoo JW, Roh HG, Moon CH, Byun HS, Kim JH (2002) Perfusion MR imaging: clinical utility for the differential diagnosis of various brain tumors. Korean J Radiol 3(3):171–179. doi:10.3348/kjr.2002.3.3.171

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kumar VA, Knopp EA, Zagzag D (2010) Magnetic resonance dynamic susceptibility-weighted contrast-enhanced perfusion imaging in the diagnosis of posterior fossa hemangioblastomas and pilocytic astrocytomas: initial results. J Comput Assist Tomogr 34(6):825–829. doi:10.1097/RCT.0b013e3181ef77e2

    Article  PubMed  Google Scholar 

  26. Chen Y, Tachibana O, Hasegawa M, Xu R, Hamada J, Yamashita J, Hashimoto N, Takahashi JA (2006) Absence of tight junctions between microvascular endothelial cells in human cerebellar hemangioblastomas. Neurosurgery 59(3):660–670 . doi:10.1227/01.neu.0000223372.18607.d7discussion 660-670

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, South Korea

    Soo Young Chae, Sangil Suh, Inseon Ryoo, Arim Park & Hae Young Seol

  2. Department of Electronic Engineering, Soonchunhyang University, Asan, South Korea

    Kyoung Jin Noh

  3. Toshiba Medical Systems Korea Co., Seoul, South Korea

    Hackjoon Shim

Authors
  1. Soo Young Chae
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sangil Suh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Inseon Ryoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arim Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kyoung Jin Noh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hackjoon Shim
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hae Young Seol
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sangil Suh.

Ethics declarations

Funding

No funding was received for this study.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in the 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

For this type of study formal consent is not required.

Electronic supplementary material

ESM 1

(PDF 474 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chae, S.Y., Suh, S., Ryoo, I. et al. A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study. Neuroradiology 59, 461–469 (2017). https://doi.org/10.1007/s00234-017-1790-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00234-017-1790-6

Keywords

Search

Navigation