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dc.contributor.authorKuijf, Hugo J.
dc.contributor.authorBiesbroek, J. Matthijs
dc.contributor.authorDe Bresser, Jeroen
dc.contributor.authorCasamitjana Díaz, Adrià
dc.contributor.authorVilaplana Besler, Verónica
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.identifier.citationKuijf, H. [et al.]. Standardized assessment of automatic segmentation of white matter hyperintensities; results of the WMH segmentation challenge. "IEEE transactions on medical imaging", Març 2019, vol. 38, núm. 11, p. 2556-2568.
dc.description© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractQuantification of white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Advanced measurements are obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge ( Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations. A secret test set of 110 images from five MR scanners was used for evaluation. Methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice Similarity Coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute percentage volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner robustness. Twenty participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods. There is one clear winner, which also has the best inter-scanner robustness. The challenge remains open for future submissions and provides a public platform for method evaluation.
dc.format.extent13 p.
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica::Aparells mèdics::Aparells de radiologia mèdica
dc.subject.lcshMagnetic resonance imaging
dc.subject.lcshBrain -- Magnetic resonance imaging
dc.subject.otherMagnetic resonance imaging (MRI)
dc.subject.otherEvaluation and performance
dc.titleStandardized assessment of automatic segmentation of white matter hyperintensities; results of the WMH segmentation challenge
dc.subject.lemacImatgeria per ressonància magnètica
dc.subject.lemacCervell -- Imatges per ressonància magnètica
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
local.citation.authorKuijf, H.; Biesbroek, J.; De Bresser, J.; Casamitjana, A.; Vilaplana, V.
local.citation.publicationNameIEEE transactions on medical imaging

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