Duque Asens, Pablo2024-05-202024-05-202020-10-06https://hdl.handle.net/20.500.14468/14523There 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.eninfo:eu-repo/semantics/openAccesstesis de maestría