Enhanced preprocessing and adaptive weighted loss function for improved for white matter hyperintensity segmentation with convolutional neural networks.
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...
| Autor: | |
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad Nacional de Educación a Distancia |
| Repositorio: | e-spacio. Repositorio Institucional de la UNED |
| Idioma: | inglés |
| OAI Identifier: | oai:e-spacio.uned.es:20.500.14468/14523 |
| Acceso en línea: | https://hdl.handle.net/20.500.14468/14523 |
| Access Level: | acceso abierto |
| Palabra clave: | 1203.04 Inteligencia artificial |
| Sumario: | 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. |
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