Skip to main content
Springer Nature Link
Log in

GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

Despite of the development of advanced segmentation techniques, achieving accurate and reproducible gross tumor volume (GTV) segmentation results is still an important challenge in neuro-radiosurgery. Nowadays, magnetic resonance imaging (MRI) is the most prominent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a minimally invasive technology for dealing with inaccessible or insufficiently treated tumors with traditional surgery or radiotherapy. During a treatment planning phase, the GTV is generally contoured by experienced neurosurgeons and radiation oncologists using fully manual segmentation procedures on MR images. Unfortunately, this operative methodology is definitely time-expensive and operator-dependent. Delineation result repeatability, in terms of both intra- and inter-operator reliability, can be achieved only by using computer-assisted approaches. In this paper a novel semi-automatic seeded image segmentation method, based on a cellular automata model, for MRI brain cancer detection and delineation is proposed. This approach, called GTVcut, employs an adaptive seed selection strategy and helps to segment the GTV, by identifying the target volume to be treated using the Gamma Knife device. The accuracy of GTVcut was evaluated on a dataset composed of 32 brain cancers, using both spatial overlap-based and distance-based metrics. The achieved experimental results are very reproducible, showing the effectiveness and the clinical feasibility of the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Adler JR Jr, Chang SD, Murphy MJ, Doty J, Geis P, Hancock SL (1998) The Cyberknife: a frameless robotic system for radiosurgery. Stereotact Funct Neurosurg 69(1–4):124–128. doi:10.1159/000099863

    Google Scholar 

  • Ambrosini RD, Wang P, O’Dell WG (2010) Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching. J Magn Reson Imaging 31(1):85–93. doi:10.1002/jmri.22009

    Article  Google Scholar 

  • Angelini ED, Clatz O, Mandonnet E, Konukoglu E, Capelle L, Duffau H (2007) Glioma dynamics and computational models: a review of segmentation, registration, and in silico growth algorithms and their clinical applications. Curr Med Imaging Rev 3(4):262–276. doi:10.2174/157340507782446241

    Article  Google Scholar 

  • Aslian H, Sadeghi M, Mahdavi SR, Babapour Mofrad F, Astarakee M, Khaledi N, Fadavi P (2013) Magnetic resonance imaging-based target volume delineation in radiation therapy treatment planning for brain tumors using localized region-based active contour. Int J Radiat Oncol Biol Phys 87(1):195–201. doi:10.1016/j.ijrobp.2013.04.049

    Article  Google Scholar 

  • Bandini S, Mauri G, Serra R (2001) Cellular automata: from a theoretical parallel computational model to its applications to complex systems. Parallel Comput 27(5):539–553. doi:10.1016/S0167-8191(00)00076-4

    Article  MathSciNet  MATH  Google Scholar 

  • Bauer S, Nolte LP, Reyes M (2011) Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Medical image computing and computer-assisted intervention (MICCAI) 2011. LNCS, vol 6893, pp 354–361. doi: 10.1007/978-3-642-23626-6_44

  • Beavis AW, Gibbs P, Dealey RA, Whitton VJ (1998) Radiotherapy treatment planning of brain tumours using MRI alone. Br J Radiol 71(845):544–548. doi:10.1259/bjr.71.845.9691900

    Article  Google Scholar 

  • Bellman R (1956) On a routing problem. Q Appl Math 16:8790

    Google Scholar 

  • Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2):191–203. doi:10.1016/0098-3004(84)90020-7

    Article  Google Scholar 

  • Boykov YY, Jolly MP (2001) Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: Eighth IEEE international conference on computer vision (ICCV) 2001, Vancouver, BC, vol 1, pp 105–112. doi: 10.1109/ICCV.2001.937505

  • Boykov YY, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239. doi:10.1109/34.969114

    Article  Google Scholar 

  • Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277. doi:10.1109/83.902291

    Article  MATH  Google Scholar 

  • Chang SD, Main W, Martin DP, Gibbs IC, Heilbrun MP (2003) An analysis of the accuracy of the Cyberknife: a robotic frameless stereotactic radiosurgical system. Neurosurgery 52(1):140–147. doi:10.1097/00006123-200301000-00018

    Google Scholar 

  • Chen C, Abdelnour-Nocera J, Wells S, Pan N (2009) Usability practice in medical imaging application development. In: HCI and usability for e-inclusion. LNCS, vol 5889, pp 405–415. doi: 10.1007/978-3-642-10308-7_29

  • Chin LS, Ma L, DiBiase S (2001) Radiation necrosis following gamma knife surgery: a case-controlled comparison of treatment parameters and long-term clinical follow up. J Neurosurg 94(6):899–904. doi:10.3171/jns.2001.94.6.0899

    Article  Google Scholar 

  • 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(2):228–247. doi:10.1016/j.ejca.2008.10.026

    Article  Google Scholar 

  • Evans PM (2008) Anatomical imaging for radiotherapy. Phys Med Biol 53(12):R151–R191. doi:10.1088/0031-9155/53/12/R01

    Article  Google Scholar 

  • Fenster A, Chiu B (2005) Evaluation of segmentation algorithms for medical imaging. In: 27th annual international conference of the engineering in medicine and biology society, IEEE-EMBS 2005, pp 7186–7189. doi: 10.1109/IEMBS.2005.1616166

  • Ford LR, Fulkerson DR (1956) Maximal flow through a network. Can J Math 8(3):399–404

    Article  MathSciNet  MATH  Google Scholar 

  • Ghosh P, Antani SK, Long LR, Thoma GR (2011) Unsupervised Grow-Cut: cellular automata-based medical image segmentation. In: First IEEE international conference on healthcare informatics, imaging and systems biology (HISB) 2011, pp 40–47. doi: 10.1109/HISB.2011.44

  • Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(11):1768–1783. doi:10.1109/TPAMI.2006.233

    Article  Google Scholar 

  • Hall LO, Bensaid AM, Clarke LP, Velthuizen RP, Silbiger MS, Bezdek JC (1992) A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 3(5):672–682. doi:10.1109/72.159057

    Article  Google Scholar 

  • Hamamci A, Unal G, Kucuk N, Engin K (2010) Cellular automata segmentation of brain tumors on post contrast MR images. In: Medical image computing and computer-assisted intervention (MICCAI) 2010. LNCS, vol 6363, pp 137–146. doi: 10.1007/978-3-642-15711-0_18

  • Hamamci A, Kucuk N, Karaman K, Engin K, Unal G (2012) Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans Med Imaging 31(3):790–804. doi:10.1109/TMI.2011.2181857

    Article  Google Scholar 

  • Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31. doi:10.1016/j.media.2016.05.004

    Article  Google Scholar 

  • Joe BN, Fukui MB, Meltzer CC, Huang QS, Day RS, Greer PJ, Bozik ME (1999) Brain tumor volume measurement: comparison of manual and semiautomated methods. Radiology 212(3):811–816. doi:10.1148/radiology.212.3.r99se22811

    Article  Google Scholar 

  • Kansal AR, Torquato S, Harsh GR, Chiocca EA, Deisboeck TS (2000) Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J Theor Biol 203(4):367–382. doi:10.1006/jtbi.2000.2000

    Article  Google Scholar 

  • Kari J (2005) Theory of cellular automata: a survey. Theor Comput Sci 334(1–3):3–33. doi:10.1016/j.tcs.2004.11.021

    Article  MathSciNet  MATH  Google Scholar 

  • Kauffmann C, Piché N (2010) Seeded ND medical image segmentation by cellular automaton on GPU. Int J Comput Assist Radiol Surg 5(3):251–262. doi:10.1007/s11548-009-0392-0

    Article  Google Scholar 

  • Khoo VS, Joon DL (2006) New developments in MRI for target volume delineation in radiotherapy. Br J Radiol 79(Special Issue 1):S2–S15. doi:10.1259/bjr/41321492

    Article  Google Scholar 

  • Lankton S, Tannenbaum A (2008) Localizing region-based active contours. IEEE Trans Image Process 17(11):2029–2039. doi:10.1109/TIP.2008.2004611

    Article  MathSciNet  MATH  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. doi:10.1038/nature145

    Article  Google Scholar 

  • Leksell L (1949) A stereotaxic apparatus for intracerebral surgery. Acta Chir Scand 99:229–233

    Google Scholar 

  • Leksell L (1951) The stereotaxic method and radiosurgery of the brain. Acta Chir Scand 102(4):316–319

    Google Scholar 

  • Levivier M, Wikler D Jr, Massager N, David P, Devriendt D, Lorenzoni J et al (2002) The integration of metabolic imaging in stereotactic procedures including radiosurgery: a review. J Neurosurg 97:42–550. doi:10.3171/jns.2002.97.supplement5.0542

    Google Scholar 

  • Luxton G, Petrovich Z, Jozsef G, Nedzi LA, Apuzzo ML (1993) Stereotactic radiosurgery: principles and comparison of treatment methods. Neurosurg 32(2):241–259. doi:10.1227/00006123-199302000-00014

    Article  Google Scholar 

  • 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  Google Scholar 

  • Meier R, Knecht U, Loosli T, Bauer S, Slotboom J, Wiest R, Reyes M (2016) Clinical evaluation of a fully-automatic segmentation method for longitudinal brain tumor volumetry. Sci Rep 6:23376. doi:10.1038/srep23376

    Article  Google Scholar 

  • Militello C, Rundo L, Vitabile S, Russo G, Pisciotta P, Marletta F, Ippolito M, D’Arrigo C, Midiri M, Gilardi MC (2015) Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised fuzzy c-means clustering. Int J Imaging Syst Technol 25(3):213–225. doi:10.1002/ima.22139

    Article  Google Scholar 

  • Miwa K, Matsuo M, Shinoda J, Aki T, Yonezawa S, Ito T, Asano Y, Yamada M, Yokoyama K, Yamada J, Yano H, Iwama T (2012) Clinical value of [11C]Methionine PET for stereotactic radiation therapy with intensity modulated radiation therapy to metastatic brain tumors. Int J Radiat Oncol Biol Phys 84(5):1139–1144. doi:10.1016/j.ijrobp.2012.02.032

    Article  Google Scholar 

  • Ohye C, Higuchi Y, Shibazaki T, Hashimoto T, Koyama T, Hirai T et al (2012) Gamma Knife thalamotomy for Parkinson disease and essential tremor: a prospective multicenter study. Neurosurg 70(3):526–536. doi:10.1227/NEU.0b013e3182350893

    Article  Google Scholar 

  • Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66. doi:10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  • Patel AA, Gawlinski ET, Lemieux SK, Gatenby RA (2001) A cellular automaton model of early tumor growth and invasion: the effects of native tissue vascularity and increased anaerobic tumor metabolism. J Theor Biol 213(3):315–331. doi:10.1006/jtbi.2001.2385

    Article  MathSciNet  Google Scholar 

  • Popovici A, Popovici D (2002) cellular automata in image processing. In: 2002 Fifteenth international symposium on mathematical theory of networks and systems, vol. 1

  • Rosin PL (2010) Image processing using 3-state cellular automata. Comput Vis Image Underst 114(7):790–802. doi:10.1016/j.cviu.2010.02.005

    Article  Google Scholar 

  • Rother C, Kolmogorov V, Blake A (2004) GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23(3):309–314. doi:10.1145/1186562.1015720

    Article  Google Scholar 

  • Rundo L, Militello C, Russo G, Pisciotta P, Valastro LM, Sabini MG, Vitabile S, Gilardi MG, Mauri G (2016a) Neuro-radiosurgery treatments: MRI brain tumor seeded image segmentation based on a cellular automata model. In: El Yacoubi S, Wąs J, Bandini S (eds) Cellular automata. Proceedings of the 12th international conference on cellular automata for research and industry-ACRI 2016, Fez, Morocco, September 5–8, 2016. LNCS, vol 9863, pp 323–333. doi: 10.1007/978-3-319-44365-2_32

  • Rundo L, Militello C, Vitabile S, Russo G, Pisciotta P, Marletta F, Ippolito M, D’Arrigo C, Midiri M, Gilardi MC (2016b) Semi-automatic brain lesion segmentation in Gamma Knife treatments using an unsupervised fuzzy c-means clustering technique. In: Advances in neural networks: computational intelligence for ICT, smart innovation, systems and technologies, vol 54, pp 15–26. Springer. doi: 10.1007/978-3-319-33747-0_2

  • Rundo L, Stefano A, Militello C, Russo G, Sabini MG, D’Arrigo C, Marletta F, Ippolito M, Mauri G, Vitabile S, Gilardi MC (2017) A fully automatic approach for multimodal PET and MR image segmentation in Gamma Knife treatment planning. Comput Methods Programs Biomed 144:77–96. doi:10.1016/j.cmpb.2017.03.011

  • Shah R, Vattoth S, Jacob R, Manzil FFP, O’Malley JP, Borghei P, Patel BN, Curé JK (2012) Radiation necrosis in the brain: imaging features and differentiation from tumor recurrence. Radiographics 32(5):1343–1359. doi:10.1148/rg.325125002

    Article  Google Scholar 

  • Sinop AK, Grady L (2007) A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: 11th IEEE international conference on computer vision, ICCV 2007, pp 1–8. doi: 10.1109/ICCV.2007.4408927

  • Soille P (2003) morphological image analysis: principles and applications, 2nd edn. Springer, New York. ISBN 3540429883

    MATH  Google Scholar 

  • Stefano A, Vitabile S, Russo G, Ippolito M, Marletta F, D’Arrigo C, D’Urso D, Sabini MG, Gambino O, Pirrone R, Ardizzone E, Gilardi MC (2015) An automatic method for metabolic evaluation of Gamma Knife treatments. In: Proceedings of the 18th International conference image analysis and processing, ICIAP, Genoa, Italy, 7–11 September 2015, Part I. LNCS, vol 9279, pp 579–589. doi: 10.1007/978-3-319-23231-7_52

  • Stefano A, Vitabile S, Russo G, Ippolito M, Marletta F, D’Arrigo C, D’Urso D, Sabini MG, Gambino O, Pirrone R, Ardizzone E, Gilardi MC (2016) A fully automatic method for biological target volume segmentation of brain metastases. Int J Imaging Syst Technol 26(1):29–37. doi:10.1002/ima.22154

    Article  Google Scholar 

  • Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15(1):1–28. doi:10.1186/s12880-015-0068-x

    Article  Google Scholar 

  • Vezhnevets V, Konouchine V (2005) GrowCut: interactive multi-label ND Image segmentation by cellular automata. In: Proceedings of the Graphicon, pp 150–156

  • von Neumann J (1966) Theory of self-reproducing automata (Edited and completed by Arthur Burks). Univ. of Illinois Press

  • Wang S, Summers RM (2012) Machine learning and radiology. Med Image Anal 16(5):933–951. doi:10.1016/j.media.2012.02.005

    Article  Google Scholar 

  • Xie K, Yang J, Zhang ZG, Zhu YM (2005) Semi-automated brain tumor and edema segmentation using MRI. Eur J Radiol 56(1):12–19. doi:10.1016/j.ejrad.2005.03.028

    Article  Google Scholar 

  • Zaitsev DA (2017) A generalized neighborhood for cellular automata. Theor Comput Sci 666:21–35. doi:10.1016/j.tcs.2016.11.002

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang YJ (2001) A review of recent evaluation methods for image segmentation. In: Proceedings of the Sixth IEEE international symposium on signal processing and its applications, ISSPA 2001, vol 1, pp 148–151. doi: 10.1109/ISSPA.2001.949797

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126, Milan, Italy

    Leonardo Rundo & Giancarlo Mauri

  2. Istituto di Bioimmagini e Fisiologia Molecolare - Consiglio Nazionale delle Ricerche (IBFM-CNR), Contrada Pietrapollastra-Pisciotto, 90015, Cefalù, PA, Italy

    Leonardo Rundo, Carmelo Militello, Giorgio Russo & Maria Carla Gilardi

  3. UOS di Fisica Sanitaria, Azienda Ospedaliera per l’Emergenza Cannizzaro, Via Messina 829, 95126, Catania, Italy

    Giorgio Russo

  4. Laboratori Nazionali del Sud (LNS), Istituto Nazionale di Fisica Nucleare (INFN), Via S. Sofia 62, 95125, Catania, Italy

    Giorgio Russo

  5. Dipartimento di Biopatologia e Biotecnologie Mediche (DIBIMED), Università degli Studi di Palermo, Via del Vespro 129, 90127, Palermo, Italy

    Salvatore Vitabile

Authors
  1. Leonardo Rundo
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Carmelo Militello
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Giorgio Russo
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Salvatore Vitabile
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Maria Carla Gilardi
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Giancarlo Mauri
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Leonardo Rundo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rundo, L., Militello, C., Russo, G. et al. GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model. Nat Comput 17, 521–536 (2018). https://doi.org/10.1007/s11047-017-9636-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-017-9636-z

Keywords