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

Duque Asens, Pablo. (2020). Enhanced preprocessing and adaptive weighted loss function for improved for white matter hyperintensity segmentation with convolutional neural networks. Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.

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Título Enhanced preprocessing and adaptive weighted loss function for improved for white matter hyperintensity segmentation with convolutional neural networks.
Autor(es) Duque Asens, Pablo
Abstract There 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.
Notas adicionales Trabajo de Fin de Máster. Máster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones. UNED
Materia(s) Ingeniería Informática
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
Director/Tutor Rincón Zamorano, Mariano
Cuadra, Jose Manuel
Fecha 2020-10-06
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-IAA-Pduque
http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-Pduque
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

 
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Creado: Wed, 22 Sep 2021, 20:09:10 CET