Skip to main content
SpringerLink
Log in

Brain Tumor Segmentation Using a Fully Convolutional Neural Network with Conditional Random Fields

  • Conference paper
  • First Online:
Book cover 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

Deep learning techniques have been widely adopted for learning task-adaptive features in image segmentation applications, such as brain tumor segmentation. However, most of existing brain tumor segmentation methods based on deep learning are not able to ensure appearance and spatial consistency of segmentation results. In this study we propose a novel brain tumor segmentation method by integrating a Fully Convolutional Neural Network (FCNN) and Conditional Random Fields (CRF), rather than adopting CRF as a post-processing step of the FCNN. We trained our network in three stages based on image patches and slices respectively. We evaluated our method on BRATS 2013 dataset, obtaining the second position on its Challenge dataset and first position on its Leaderboard dataset. Compared with other top ranking methods, our method could achieve competitive performance with only three imaging modalities (Flair, T1c, T2), rather than four (Flair, T1, T1c, T2), which could reduce the cost of data acquisition and storage. Besides, our method could segment brain images slice-by-slice, much faster than the methods patch-by-patch. We also took part in BRATS 2016 and got satisfactory results. As the testing cases in BRATS 2016 are more challenging, we added a manual intervention post-processing system during our participation.

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

Notes

  1. 1.

    https://www.virtualskeleton.ch/BRATS/Start2013.

  2. 2.

    http://martinos.org/qtim/miccai2013/results.html.

  3. 3.

    http://braintumorsegmentation.org/.

References

  1. Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993–2024 (2015)

    Article  Google Scholar 

  2. Bauer, S., Wiest, R., Nolte, L.-P., Reyes, M.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58, 97–129 (2013)

    Article  Google Scholar 

  3. Goetz, M., Weber, C., Binczyk, F., Polanska, J., Tarnawski, R., Bobek-Billewicz, B., Koethe, U., Kleesiek, J., Stieltjes, B., Maier-Hein, K.H.: DALSA: domain adaptation for supervised learning from sparsely annotated MR images. IEEE Trans. Med. Imaging 35, 184–196 (2016)

    Article  Google Scholar 

  4. Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8, 275–283 (2004)

    Article  Google Scholar 

  5. Cobzas, D., Birkbeck, N., Schmidt, M., Jagersand, M., Murtha, A.: 3D variational brain tumor segmentation using a high dimensional feature set. In: IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)

    Google Scholar 

  6. Goetz, M., Weber, C., Bloecher, J., Stieltjes, B., Meinzer, H.-P., Maier-Hein, K.: Extremely randomized trees based brain tumor segmentation. In: Proceedings MICCAI BraTS (Brain Tumor Segmentation Challenge), pp. 6–11 (2014)

    Google Scholar 

  7. Kleesiek, J., Biller, A., Urban, G., Kothe, U., Bendszus, M., Hamprecht, F.: Ilastik for multi-modal brain tumor segmentation. In: Proceedings MICCAI BraTS (Brain Tumor Segmentation Challenge), pp. 12–17 (2014)

    Google Scholar 

  8. Davy, A., Havaei, M., Warde-farley, D., Biard, A., Tran, L., Jodoin, P.-M., Courville, A., Larochelle, H., Pal, C., Bengio, Y.: Brain tumor segmentation with deep neural networks. In: Proceedings MICCAI BraTS (Brain Tumor Segmentation Challenge), pp. 1–5 (2014)

    Google Scholar 

  9. Urban, G., Bendszus, M., Hamprecht, F., Kleesiek, J.: Multi-modal brain tumor segmentatioin using deep convolutional neural networks. In: Proceedings MICCAI BraTS (Brain Tumor Segmentation Challenge), pp. 31–35 (2014)

    Google Scholar 

  10. Zikic, D., Ioannou, Y., Brown, M., Criminisi, A.: Segmentation of brain tumor tissues with convolutional neural networks. In: Proceedings MICCAI BraTS (Brain Tumor Segmentation Challenge), pp. 36–39 (2014)

    Google Scholar 

  11. Dvorak, P., Menze, B.H.: Structured prediction with convolutional neural networks for multimodal brain tumor segmentation. In: Proceedings MICCAI BraTS (Brain Tumor Segmentation Challenge), pp. 13–24 (2015)

    Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. Vaidhya, K., Thirunavukkarasu, S., Alex, V., Krishnamurthi, G.: Multi-modal brain tumor segmentation using stacked denoising autoencoders. In: Proceedings MICCAI BraTS (Brain Tumor Segmentation Challenge), pp. 60–64 (2015)

    Google Scholar 

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

    Article  Google Scholar 

  15. Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1537 (2015)

    Google Scholar 

  16. Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010)

    Article  Google Scholar 

  17. Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. NIPS (2011)

    Google Scholar 

  18. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678 (2014)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National High Technology Research and Development Program of China (2015AA020504) and the National Natural Science Foundation of China under Grant No. 61572499, 61421004.

Author information

Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Xiaomei Zhao & Yihong Wu

  2. Beijing Neurosurgical Institute, Capital Medical University, Beijing, China

    Guidong Song & Yazhuo Zhang

  3. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

    Zhenye Li & Yazhuo Zhang

  4. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA

    Yong Fan

  5. Beijing Institute for Brain Disorders Brain Tumor Center, Beijing, China

    Yazhuo Zhang

  6. China National Clinical Research Center for Neurological Diseases, Beijing, China

    Yazhuo Zhang

Authors
  1. Xiaomei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yihong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guidong Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhenye Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yong Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yazhuo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiaomei Zhao or Yihong Wu .

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

Zhao, X., Wu, Y., Song, G., Li, Z., Fan, Y., Zhang, Y. (2016). Brain Tumor Segmentation Using a Fully Convolutional Neural Network with Conditional Random Fields. 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_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55524-9_8

  • 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