Skip to main content
SpringerLink
Log in

Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework

  • Conference paper
  • First Online:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10154))

Abstract

We present an approach for segmenting both low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed framework is an extension of our previous work [6, 7], with an additional component for segmenting post-operative scans. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative model based on a joint segmentation-registration framework is used to segment the brain scans into cancerous and healthy tissues. Secondly, a gradient boosting classification scheme is used to refine tumor segmentation based on information from multiple patients. We evaluated our approach in 218 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2016 challenge and report promising results. During the testing phase, the proposed approach was ranked among the top performing methods, after being additionally evaluated in 191 unseen cases.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agn, M., Puonti, O., Rosenschöld, P.M., Law, I., Leemput, K.: Brain tumor segmentation using a generative model with an RBM prior on tumor shape. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 168–180. Springer, Cham (2016). doi:10.1007/978-3-319-30858-6_15

    Chapter  Google Scholar 

  2. Akbari, H., Macyszyn, L., Da, X., Wolf, R.L., Bilello, M., Verma, R., O’Rourke, D.M., Davatzikos, C.: Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity. Radiology 273(2), 502–510 (2014)

    Article  Google Scholar 

  3. Ayachi, R., Ben Amor, N.: Brain tumor segmentation using support vector machines. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS (LNAI), vol. 5590, pp. 736–747. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02906-6_63

    Chapter  Google Scholar 

  4. Bakas, S., Chatzimichail, K., Hunter, G., Labbe, B., Sidhu, P.S., Makris, D.: Fast semi-automatic segmentation of focal liver lesions in contrast-enhanced ultrasound, based on a probabilistic model. In: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp. 1–10 (2015)

    Google Scholar 

  5. Bakas, S., Labbe, B., Hunter, G.J.A., Sidhu, P., Chatzimichail, K., Makris, D.: Fast segmentation of focal liver lesions in contrast-enhanced ultrasound data. In: Proceedings of the 18th Annual Conference on Medical Image Understanding and Analysis (MIUA), pp. 73–78 (2014)

    Google Scholar 

  6. Bakas, S., Zeng, K., Sotiras, A., Rathore, S., Akbari, H., Gaonkar, B.,Rozycki, M., Pati, S., Davazikos, C.: Segmentation of gliomas in multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework. In: Proceedings of the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2015, pp. 5–12 (2015)

    Google Scholar 

  7. Bakas, S., Zeng, K., Sotiras, A., Rathore, S., Akbari, H., Gaonkar, B., Rozycki, M., Pati, S., Davatzikos, C.: GLISTRboost: combining multimodal MRI segmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 144–155. Springer, Cham (2016). doi:10.1007/978-3-319-30858-6_13

    Chapter  Google Scholar 

  8. Deeley, M.A., Chen, A., Datteri, R., Noble, J.H., Cmelak, A.J., Donnelly, E.F., Malcolm, A.W., Moretti, L., Jaboin, J., Niermann, K., Yang, E.S., Yu, D.S., Yei, F., Koyama, T., Ding, G.X., Dawant, B.M.: Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study. Phys. Med. Biol. 56(14), 4557–4577 (2011)

    Article  Google Scholar 

  9. Deschamps, T., Cohen, L.D.: Fast extraction of minimal paths in 3D images and applications to virtual endoscopy. Med. Image Anal. 5(4), 281–299 (2001)

    Article  Google Scholar 

  10. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  11. Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  12. Gaonkar, B., Macyszyn, L., Bilello, M., Sadaghiani, M.S., Akbari, H., Attiah, M.A., Ali, Z.S., Da, X., Zhan, Y., O’Rourke, D., Grady, S.M., Davatzikos, C.: Automated tumor volumetry using computer-aided image segmentation. Acad. Radiol. 22(5), 653–661 (2015)

    Article  Google Scholar 

  13. Gering, D.T., Grimson, W.E.L., Kikinis, R.: Recognizing deviations from normalcy for brain tumor segmentation. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 388–395. Springer, Heidelberg (2002). doi:10.1007/3-540-45786-0_48

    Chapter  Google Scholar 

  14. Gooya, A., Biros, G., Davatzikos, C.: Deformable registration of glioma images using EM algorithm and diffusion reaction modeling. IEEE Trans. Med. Imaging 30(2), 375–390 (2011)

    Article  Google Scholar 

  15. Gooya, A., Pohl, K.M., Bilello, M., Biros, G., Davatzikos, C.: Joint segmentation and deformable registration of brain scans guided by a tumor growth model. Med. Image Comput. Comput.-Assist. Interventions 14(2), 532–540 (2011)

    Google Scholar 

  16. Gooya, A., Pohl, K.M., Bilello, M., Cirillo, L., Biros, G., Melhem, E.R., Davatzikos, C.: GLISTR: glioma image segmentation and registration. IEEE Trans. Med. Imaging 31(10), 1941–1954 (2012)

    Article  Google Scholar 

  17. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)

    Article  Google Scholar 

  18. Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017). http://www.sciencedirect.com/science/article/pii/S1361841516300330

    Article  Google Scholar 

  19. Hogea, C., Davatzikos, C., Biros, G.: An image-driven parameter estimation problem for a reaction-diffusion glioma growth model with mass effects. J. Math. Biol. 56(6), 793–825 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  20. Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A., Criminisi, A., Rueckert, D., Glocker, B.: Deepmedic on brain tumor segmentation

    Google Scholar 

  21. Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017). http://www.sciencedirect.com/science/article/pii/S1361841516301839

    Article  Google Scholar 

  22. Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., Büchler, P.: The virtual skeleton database: an open access repository for biomedical research and collaboration. J. Med. Internet Res. 15(11), e245 (2013)

    Article  Google Scholar 

  23. Kwon, D., Akbari, H., Da, X., Gaonkar, B., Davatzikos, C.: Multimodal brain tumor image segmentation using GLISTR. In: MICCAI Brain Tumor Segmentation (BraTS) Challenge Manuscripts, pp. 18–19 (2014)

    Google Scholar 

  24. Kwon, D., Niethammer, M., Akbari, H., Bilello, M., Davatzikos, C., Pohl, K.M.: Portr: pre-operative and post-recurrence brain tumor registration. IEEE Trans. Med. Imaging 33(3), 651–667 (2014)

    Article  Google Scholar 

  25. Kwon, D., Shinohara, R.T., Akbari, H., Davatzikos, C.: Combining generative models for multifocal glioma segmentation and registration. Med. Image Comput. Comput.-Assist. Interventions 17(1), 763–770 (2014)

    Google Scholar 

  26. Kwon, D., Zeng, K., Bilello, M., Davatzikos, C.: Estimating patient specific templates for pre-operative and follow-up brain tumor registration. Med. Image Comput. Comput.-Assist. Interventions 2, 222–229 (2015)

    Google Scholar 

  27. Lee, C.-H., Wang, S., Murtha, A., Brown, M.R.G., Greiner, R.: Segmenting brain tumors using pseudo–conditional random fields. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5241, pp. 359–366. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85988-8_43

    Chapter  Google Scholar 

  28. Louis, D.N.: Molecular pathology of malignant gliomas. Ann. Rev. Pathol. Mech. Dis. 1, 97–117 (2006)

    Article  Google Scholar 

  29. Menze, B.H., et al.: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015). doi:10.1109/TMI.2014.2377694

    Article  Google Scholar 

  30. Moon, N., Bullitt, E., van Leemput, K., Gerig, G.: Model-based brain and tumor segmentation. In: Object Recognition Supported by User Interaction for Service Robots. vol. 1, pp. 528–531 (2002)

    Google Scholar 

  31. Nyul, L.G., Udupa, J.K., Zhang, X.: New variants of a method of mri scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)

    Article  Google Scholar 

  32. Parisot, S., Duffau, H., Chemouny, S., Paragios, N.: Joint tumor segmentation and dense deformable registration of brain MR images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 651–658. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33418-4_80

    Chapter  Google Scholar 

  33. Pati, S., Rathore, S., Kalarot, R., Sridharan, P., Bergman, M., Shinohara, T., Yushkevich, P., Fan, Y., Verma, R., Kontos, D., Davatzikos, C.: Cancer and Phenomics Toolkit (CAPTk): a software suite for computational oncology and radiomics. In: Radiological Society of North America 2016 Scientific Assembly and Annual Meeting, November 27 - December 2, 2016, Chicago IL (2016). http://archive.rsna.org/2016/16014589.html

  34. Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)

    Article  Google Scholar 

  35. Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8(3), 275–283 (2004). http://dx.doi.org/10.1016/j.media.2004.06.007

    Article  Google Scholar 

  36. Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. Proc. Nat. Acad. Sci. U.S.A. 93(4), 1591–1595 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  37. Smith, S.M., Brady, J.M.: SUSAN - a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)

    Article  Google Scholar 

  38. Wen, P.Y., Kesari, S.: Malignant gliomas in adults. New England J. Med. 359(5), 492–507 (2008)

    Article  Google Scholar 

  39. Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O.M., Das, T., Jena, R., Price, S.J.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 369–376. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33454-2_46

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Section of Biomedical Image Analysis, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA

    Ke Zeng, Spyridon Bakas, Aristeidis Sotiras, Hamed Akbari, Martin Rozycki, Saima Rathore, Sarthak Pati & Christos Davatzikos

Authors
  1. Ke Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Spyridon Bakas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aristeidis Sotiras
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hamed Akbari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Martin Rozycki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Saima Rathore
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sarthak Pati
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Christos Davatzikos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Ke Zeng or Spyridon Bakas .

Editor information

Editors and Affiliations

  1. University of Zurich, Istituto Italiano di Tecnologia, Genoa, Italy

    Alessandro Crimi

  2. TU München, Computer Science, Munich, Germany

    Bjoern Menze

  3. Medical Informatics, University of Lübeck, Lübeck, Germany

    Oskar Maier

  4. Surgical Technology and Biomechanics, Universität Bern, Bern, Switzerland

    Mauricio Reyes

  5. Department of Medicine, University of Cambridge, Cambridge, United Kingdom

    Stefan Winzeck

  6. Institute of Medical Informatics, University of Lübeck, Lübeck, Germany

    Heinz Handels

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Zeng, K. et al. (2016). Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55524-9_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55523-2

  • Online ISBN: 978-3-319-55524-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation