Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering

Abstract

Perivascular Spaces (PVS) are a feature of Small Vessel Disease (SVD), and are an important part of the brain’s circulation and glymphatic drainage system. Quantitative analysis of PVS on Magnetic Resonance Images (MRI) is important for understanding their relationship with neurological diseases. In this work, we propose a segmentation technique based on the 3D Frangi filtering for extraction of PVS from MRI. We used ordered logit models and visual rating scales as alternative ground truth for Frangi filter parameter optimization and evaluation. We optimized and validated our proposed models on two independent cohorts, a dementia sample (N = 20) and patients who previously had mild to moderate stroke (N = 48). Results demonstrate the robustness and generalisability of our segmentation method. Segmentation-based PVS burden estimates correlated well with neuroradiological assessments (Spearman’s ρ = 0.74, p < 0.001), supporting the potential of our proposed method.

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References 27

1. 1.

   Wardlaw JM, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology. 2013;12:822-838.

   Not in our library10.1016/s1474-4422(13)70124-8

2. 2.

   Ramirez J, et al. Imaging the Perivascular Space as a Potential Biomarker of Neurovascular and Neurodegenerative Diseases. Cellular and Molecular Neurobiology. 2016;36:289-299.

   Not in our library10.1007/s10571-016-0343-6

3. 3.

   Valdés Hernández MdC, Piper RJ, Wang X, Deary IJ, Wardlaw JM. Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images: A systematic review. Journal of Magnetic Resonance Imaging. 2013;38:774-785.

   Not in our library10.1002/jmri.24047

4. 4.

   Doubal FN, Maclullich AMJ, Ferguson KJ, Dennis MS, Wardlaw JM. Enlarged Perivascular Spaces on MRI Are a Feature of Cerebral Small Vessel Disease. Stroke. 2010;41:450-454.

   Not in our library10.1161/strokeaha.109.564914

5. 5.

   Arba F, et al. Enlarged perivascular spaces and cognitive impairment after stroke and transient ischemic attack. International Journal of Stroke. 2016;0:1-10.

   Not in our library10.1177/1747493016666091

6. 6.
   Enlarged Perivascular Spaces and Cerebral Small Vessel Disease · 2013

   No notice
7. 7.
   Cerebral Perivascular Spaces Visible on Magnetic Resonance Imaging: Development of a Qualitative Rating Scale and its Observer Reliability · 2015

   No notice
8. 8.

   MacLullich AMJ, et al. Enlarged perivascular spaces are associated with cognitive function in healthy elderly men. Journal of neurology, neurosurgery, and psychiatry. 2004;75:1519-1523.

   Not in our library10.1136/jnnp.2003.030858

9. 9.

   Descombes X, Kruggel F, Wollny G, Gertz HJ. An Object-Based Approach for Detecting Small Brain Lesions: Application to Virchow-Robin Spaces. IEEE Transactions on Medical Imaging. 2004;23:246-255.

   Not in our library10.1109/tmi.2003.823061

10. 10.

    Ramirez J, et al. Visible Virchow-Robin spaces on magnetic resonance imaging of Alzheimer’s disease patients and normal elderly from the Sunnybrook Dementia study. Journal of Alzheimer’s Disease. 2015;43:415-24.

    Not in our library10.3233/jad-132528

11. 11.

    Wang X, et al. Development and initial evaluation of a semi-automatic approach to assess perivascular spaces on conventional magnetic resonance images. Journal of Neuroscience Methods. 2016;257:34-44.

    Not in our library10.1016/j.jneumeth.2015.09.010

12. 12.

    Frangi, A., Niessen, W., Vincken, K. & Viergever, M. Multiscale vessel enhancement filtering. Medical Image Computing and Computer-Assisted Intervention (MICCAI98) 130–137 (1998).

    Not in our library

13. 13.

    Criminisi A, Shotton J, Konukoglu E. Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning. Microsoft Research Cambridge, Tech. Rep. MSRTR-2011-114. 2011;5:12.

    Not in our library

14. 14.

    Zong X, Park SH, Shen D, Lin W. Visualization of perivascular spaces in the human brain at 7T: Sequence optimization and morphology characterization. NeuroImage. 2015;125:895-902.

    Not in our library10.1016/j.neuroimage.2015.10.078

15. 15.

    Park SH, Zong X, Gao Y, Lin W, Shen D. Segmentation of Perivascular Spaces in 7T MR Image using Auto-Context Model with Orientation-Normalized Features. NeuroImage. 2016;134:223-235.

    Not in our library10.1016/j.neuroimage.2016.03.076

16. 16.

    Hou Y, et al. Enhancement of Perivascular Spaces in 7T MR Image using Haar Transform of Non-local Cubes and Block-matching Filtering. Scientific Reports. 2017;7:8569.

    Not in our library10.1038/s41598-017-09336-5

17. 17.

    Greene, W. H. & Hensher, D. A. Modeling ordered choices: A primer (Cambridge University Press, 2010).

    Not in our library

18. 18.

    Valdés Hernández MdC, et al. Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke. Brain and Behavior. 2015;415:1-18.

    Not in our library10.1002/brb3.415

19. 19.

    Lupascu CA, Tegolo D, Trucco E. Fabc: Retinal vessel segmentation using adaboost. IEEE Transactions on Information Technology in Biomedicine. 2010;14:1267-1274.

    Not in our library10.1109/titb.2010.2052282

20. 20.

    Ramirez J, et al. Lesion Explorer: A comprehensive segmentation and parcellation package to obtain regional volumetrics for subcortical hyperintensities and intracranial tissue. NeuroImage. 2011;54:963-973.

    Not in our library10.1016/j.neuroimage.2010.09.013

21. 21.

    Patankar TF, et al. Dilatation of the Virchow-Robin space is a sensitive indicator of cerebral microvascular disease: Study in elderly patients with dementia. American Journal of Neuroradiology. 2005;26:1512-1520.

    Not in our library

22. 22.

    Bezdek J, Hall L, Clark M, Goldgof DB, Clarke L. Medical image analysis with fuzzy models. Statistical Methods in Medical Research. 1997;6:191-214.

    Not in our library10.1177/096228029700600302

23. 23.

    Ballerini L, et al. Application of the Ordered Logit Model to Optimising Frangi Filter Parameters for Segmentation of Perivascular Spaces. Procedia Computer Science. 2016;90:61-67.

    Not in our library10.1016/j.procs.2016.07.011

24. 24.
    Robust Correlation Analyses: False Positive and Power Validation Using a New Open Source Matlab Toolbox · 2013

    No notice
25. 25.
    Optimized Brain Extraction for Pathological Brains (optiBET) · 2014

    No notice
26. 26.

    Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging. 2001;20:45-57.

    Not in our library10.1109/42.906424

27. 27.

    Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage. 2002;17:825-841.

    Not in our library10.1006/nimg.2002.1132

Cited by papers we hold 3

• Cerebral small vessel disease burden and longitudinal cognitive decline from age 73 to 82: the Lothian Birth Cohort 1936 · 2021
  Not checked
• Perivascular spaces and brain waste clearance systems: relevance for neurodegenerative and cerebrovascular pathology · 2021
  No notice
• Image processing approaches to enhance perivascular space visibility and quantification using MRI · 2019
  No notice

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