Skip to main content

Segmentation of Perivascular Spaces Using Vascular Features and Structured Random Forest from 7T MR Image

  • Conference paper
  • First Online:
Machine Learning in Medical Imaging (MLMI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10019))

Included in the following conference series:


Quantitative analysis of perivascular spaces (PVSs) is important to reveal the correlations between cerebrovascular lesions and neurodegenerative diseases. In this study, we propose a learning-based segmentation framework to extract the PVSs from high-resolution 7T MR images. Specifically, we integrate three types of vascular filter responses into a structured random forest for classifying voxels into PVS and background. In addition, we also propose a novel entropy-based sampling strategy to extract informative samples in the background for training the classification model. Since various vascular features can be extracted by the three vascular filters, even thin and low-contrast structures can be effectively extracted from the noisy background. Moreover, continuous and smooth segmentation results can be obtained by utilizing the patch-based structured labels. The segmentation performance is evaluated on 19 subjects with 7T MR images, and the experimental results demonstrate that the joint use of entropy-based sampling strategy, vascular features and structured learning improves the segmentation accuracy, with the Dice similarity coefficient reaching 66 %.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
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

Similar content being viewed by others


  1. Descombes, X., Kruggel, F., Wollny, G., Gertz, H.J.: An object-based approach for detecting small brain lesions: application to Virchow-Robin spaces. IEEE Trans. Med. Imaging 23(2), 246–255 (2004)

    Article  Google Scholar 

  2. Uchiyama, Y., Kunieda, T., Asano, T., Kato, H., Hara, T., Kanematsu, M., Iwama, T., Hoshi, H., Kinosada, Y., Fujita, H.: Computer-aided diagnosis scheme for classification of lacunar infarcts and enlarged virchow-robin spaces in brain MR images. In: Engineering in Medicine and Biology Society, pp. 3908–3911. IEEE (2008)

    Google Scholar 

  3. Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007)

    Article  Google Scholar 

  4. Marín, D., Aquino, A., Gegúndez-Arias, M.E., Bravo, J.M.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146–158 (2011)

    Article  Google Scholar 

  5. Hernández, M., Piper, R.J., Wang, X., Deary, I.J., Wardlaw, J.M.: Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images: a systematic review. J. Magn. Reson. Imaging 38(4), 774–785 (2013)

    Article  Google Scholar 

  6. Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 9, 891–906 (1991)

    Article  Google Scholar 

  7. Zhang, J., Liang, J., Zhao, H.: Local energy pattern for texture classification using self-adaptive quantization thresholds. IEEE Trans. Image Process. 22(1), 31–42 (2013)

    Article  MathSciNet  Google Scholar 

  8. Derpanis, K.G., Wildes, R.P.: Dynamic texture recognition based on distributions of spacetime oriented structure. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 191–198. IEEE (2010)

    Google Scholar 

  9. Zhang, J., Zhao, H., Liang, J.: Continuous rotation invariant local descriptors for texton dictionary-based texture classification. Comput. Vis. Image Underst. 117(1), 56–75 (2013)

    Article  MathSciNet  Google Scholar 

  10. Law, M.W.K., Chung, A.C.S.: Three dimensional curvilinear structure detection using optimally oriented flux. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 368–382. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88693-8_27

    Chapter  Google Scholar 

  11. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)

    Google Scholar 

  12. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Dinggang Shen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Zhang, J., Gao, Y., Park, S.H., Zong, X., Lin, W., Shen, D. (2016). Segmentation of Perivascular Spaces Using Vascular Features and Structured Random Forest from 7T MR Image. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47156-3

  • Online ISBN: 978-3-319-47157-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics