Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification

Ben Philps; Maria Del C. Valdes Hernandez; Chen Qin; Una Clancy; Eleni Sakka; Susana Munoz Maniega; Mark E. Bastin; Angela C.C. Jochems; Joanna M. Wardlaw; Miguel O. Bernabeu; Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification

doi:10.1016/j.media.2025.103697

Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification

Ben Philps, Maria Del C. Valdes Hernandez, Chen Qin, Una Clancy, Eleni Sakka, Susana Munoz Maniega, Mark E. Bastin, Angela C.C. Jochems, Joanna M. Wardlaw, Miguel O. Bernabeu, Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	103697
Journal	Medical Image Analysis
Early online date	12 Jul 2025
DOIs	https://doi.org/10.1016/j.media.2025.103697
Publication status	E-pub ahead of print - 12 Jul 2025

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1-s2.0-S1361841525002440-mainFinal published version, 4.07 MBLicence: CC BY

https://linkinghub.elsevier.com/retrieve/pii/S1361841525002440

Cite this

Philps, B., Valdes Hernandez, M. D. C., Qin, C., Clancy, U., Sakka, E., Maniega, S. M., Bastin, M. E., Jochems, A. C. C., Wardlaw, J. M., Bernabeu, M. O., & Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification (2025). Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification. Medical Image Analysis, 103697. Advance online publication. https://doi.org/10.1016/j.media.2025.103697

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title = "Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification",

author = "Ben Philps and \{Valdes Hernandez\}, \{Maria Del C.\} and Chen Qin and Una Clancy and Eleni Sakka and Maniega, \{Susana Munoz\} and Bastin, \{Mark E.\} and Jochems, \{Angela C.C.\} and Wardlaw, \{Joanna M.\} and Bernabeu, \{Miguel O.\} and \{Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification\}",

year = "2025",

month = jul,

day = "12",

doi = "10.1016/j.media.2025.103697",

language = "English",

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journal = "Medical Image Analysis",

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Philps, B, Valdes Hernandez, MDC, Qin, C, Clancy, U , Sakka, E , Maniega, SM , Bastin, ME , Jochems, ACC , Wardlaw, JM , Bernabeu, MO & Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification 2025, 'Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification', Medical Image Analysis, pp. 103697. https://doi.org/10.1016/j.media.2025.103697

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T1 - Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification

AU - Philps, Ben

AU - Valdes Hernandez, Maria Del C.

AU - Qin, Chen

AU - Clancy, Una

AU - Sakka, Eleni

AU - Maniega, Susana Munoz

AU - Bastin, Mark E.

AU - Jochems, Angela C.C.

AU - Wardlaw, Joanna M.

AU - Bernabeu, Miguel O.

AU - Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification

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DO - 10.1016/j.media.2025.103697

M3 - Article

SN - 1361-8415

SP - 103697

JO - Medical Image Analysis

JF - Medical Image Analysis

ER -