Deep learning for atrophy quantification in brain magnetic resonance imaging
The quantification of cerebral atrophy is fundamental in neuroinformatics since it permits diagnosing brain diseases, assessing their progression, and determining the effectiveness of novel treatments to counteract them. However, this is still an open and challenging problem since the performance 2/...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/671699 |
| Acceso en línea: | http://hdl.handle.net/10803/671699 |
| Access Level: | acceso abierto |
| Palabra clave: | Aprenentatge profund Aprendizaje profundo Deep learning Atròfia cerebral Atrofia cerebral Cerebral atrophy Quantificació Cuantificación Quantification Xarxes neuronals convolucionals Redes neuronales convolucionales Convolutional neural networks Segmentació de teixits Segmentación de tejidos Tissue segmentation Imatges per ressonància magnètica Imágenes por resonancia magnética Magnetic resonance imaging Ressonància magnètica cerebral Resonancia magnética cerebral Brain MRI 004 615 616.8 |
| Sumario: | The quantification of cerebral atrophy is fundamental in neuroinformatics since it permits diagnosing brain diseases, assessing their progression, and determining the effectiveness of novel treatments to counteract them. However, this is still an open and challenging problem since the performance 2/2 of traditional methods depends on imaging protocols and quality, data harmonisation errors, and brain abnormalities. In this doctoral thesis, we question whether deep learning methods can be used for better estimating cerebral atrophy from magnetic resonance images. Our work shows that deep learning can lead to a state-of-the-art performance in cross-sectional assessments and compete and surpass traditional longitudinal atrophy quantification methods. We believe that the proposed cross-sectional and longitudinal methods can be beneficial for the research and clinical community |
|---|