Resumen de 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 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 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.
