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Enhanced preprocessing and adaptive weighted loss function for improved for white matter hyperintensity segmentation with convolutional neural networks.

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dc.contributor.authorDuque Asens, Pablo
dc.contributor.directorRincón Zamorano, Mariano
dc.contributor.directorCuadra Troncoso, José Manuel
dc.date.accessioned2024-05-20T12:33:55Z
dc.date.available2024-05-20T12:33:55Z
dc.date.issued2020-10-06
dc.description.abstractThere is a great interest in automating White Matter Hyperintensities (WMH) segmentation due to their importance in the medical eld as well as the great amount of inter- and intra-observer variability that appears when it is manually segmented in magnetic resonance imaging. In this work we present a multistep tailored preprocessing consisting mainly of brain extraction, intensity contrast enhancement, subject based slice cropping and intensity standardization. The segmentation task is then performed by a fully convolutional neural network with attention gates which employs a customized loss function based on the dice similarity coecient and the F1 score. Experimental results on the white matter hyperintensities segmentation challenge [Kuijf et al., 2019] show that our proposed preprocessing improves segmentation, that attention gated U-Net further improves segmentation tasks compared to the original U-Net and our proposed loss function has the potential to improve lesion-wise F1 on DSC based segmentations.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14523
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.degreeMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.titleEnhanced preprocessing and adaptive weighted loss function for improved for white matter hyperintensity segmentation with convolutional neural networks.es
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dc.typemaster thesisen
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