Advertisement

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

  • Jun Zhang
  • Yaozong Gao
  • Sang Hyun Park
  • Xiaopeng Zong
  • Weili Lin
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)

Abstract

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 %.

Keywords

Random Forest Random Forest Model Dice Similarity Coefficient Local Entropy Segmentation Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 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)CrossRefGoogle Scholar
  2. 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. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 9, 891–906 (1991)CrossRefGoogle Scholar
  7. 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)MathSciNetCrossRefGoogle Scholar
  8. 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. 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)MathSciNetCrossRefGoogle Scholar
  10. 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 CrossRefGoogle Scholar
  11. 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. 12.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jun Zhang
    • 1
  • Yaozong Gao
    • 1
    • 2
  • Sang Hyun Park
    • 1
  • Xiaopeng Zong
    • 1
  • Weili Lin
    • 1
  • Dinggang Shen
    • 1
    • 3
    Email author
  1. 1.Department of Radiology and BRICUNC at Chapel HillChapel HillUSA
  2. 2.Department of Computer ScienceUNC at Chapel HillChapel HillUSA
  3. 3.Department of Brain and Cognitive EngineeringKorea UniversitySeongbuk-guRepublic of Korea

Personalised recommendations