Influence of threshold selection and image sequence in in-vivo segmentation of enlarged perivascular spaces
Valdés Hernández MdC, Duarte Coello R, Xu W, Bernal J, Cheng Y, Ballerini L, Wiseman SJ, Chappell FM, Clancy U, Jaime García D, Arteaga Reyes C, Zhang JF, Liu X, Hewins W, Stringer M, Doubal F, Thrippleton MJ, Jochems A, Brown R, Wardlaw JM (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 403
Article Number: 110037
DOI: 10.1016/j.jneumeth.2023.110037
Abstract
Background: Growing interest surrounds perivascular spaces (PVS) as a clinical biomarker of brain dysfunction given their association with cerebrovascular risk factors and disease. Neuroimaging techniques allowing quick and reliable quantification are being developed, but, in practice, they require optimisation as their limits of validity are usually unspecified. New method: We evaluate modifications and alternatives to a state-of-the-art (SOTA) PVS segmentation method that uses a vesselness filter to enhance PVS discrimination, followed by thresholding of its response, applied to brain magnetic resonance images (MRI) from patients with sporadic small vessel disease acquired at 3 T. Results: The method is robust against inter-observer differences in threshold selection, but separate thresholds for each region of interest (i.e., basal ganglia, centrum semiovale, and midbrain) are required. Noise needs to be assessed prior to selecting these thresholds, as effect of noise and imaging artefacts can be mitigated with a careful optimisation of these thresholds. PVS segmentation from T1-weighted images alone, misses small PVS, therefore, underestimates PVS count, may overestimate individual PVS volume especially in the basal ganglia, and is susceptible to the inclusion of calcified vessels and mineral deposits. Visual analyses indicated the incomplete and fragmented detection of long and thin PVS as the primary cause of errors, with the Frangi filter coping better than the Jerman filter. Comparison with existing methods: Limits of validity to a SOTA PVS segmentation method applied to 3 T MRI with confounding pathology are given. Conclusions: Evidence presented reinforces the STRIVE-2 recommendation of using T2-weighted images for PVS assessment wherever possible. The Frangi filter is recommended for PVS segmentation from MRI, offering robust output against variations in threshold selection and pathology presentation.
Involved external institutions
University of Edinburgh
United Kingdom (GB)
West China Medical Center of Sichuan University / 四川大学华西医学中心
China (CN)
The University of Hong Kong (HKU) / 香港大學
Hong Kong (HK)
United Kingdom (GB)
West China Medical Center of Sichuan University / 四川大学华西医学中心
China (CN)
The University of Hong Kong (HKU) / 香港大學
Hong Kong (HK)
How to cite
APA:
Valdés Hernández, M.d.C., Duarte Coello, R., Xu, W., Bernal, J., Cheng, Y., Ballerini, L.,... Wardlaw, J.M. (2024). Influence of threshold selection and image sequence in in-vivo segmentation of enlarged perivascular spaces. Journal of Neuroscience Methods, 403. https://doi.org/10.1016/j.jneumeth.2023.110037
MLA:
Valdés Hernández, Maria del C., et al. "Influence of threshold selection and image sequence in in-vivo segmentation of enlarged perivascular spaces." Journal of Neuroscience Methods 403 (2024).
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