Deep learning for atrophy quantification in brain magnetic resonance imaging

• Autores: Jose Bernal Moyano
• Directores de la Tesis: Xavier Lladó Bardera (dir. tes.), Arnau Oliver i Malagelada (codir. tes.)
• Lectura: En la Universitat de Girona ( España ) en 2020
• Idioma: español
• Tribunal Calificador de la Tesis: Joan Martí Bonmatí (presid.), Veronica Vilaplana Besler (secret.), Arrate Muñoz Barrutia (voc.)
• Programa de doctorado: Programa de Doctorado en Tecnología por la Universidad de Girona
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • 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.