News

DOCTORAL THESIS: Deep learning for atrophy quantification in brain magnetic resonance imaging

By Jose Bernal Moyano Supervised by Dr. Arnau Oliver / Dr. Xavier Lladó   Abstract Cerebral atrophy is a neuroimaging feature of ageing and diverse brain pathologies that indicate of loss of neurons and their connections. Its quantification plays a fundamental role in neuroinformatics since it permits studying brain development, diagnosing brain…

DOCTORAL THESIS: Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques

By Kaisar Kushibar Supervised by Dr. Sergi Valverde / Dr. Arnau Oliver / Dr. Xavier Lladó   Abstract The sub-cortical brain structures are located beneath the cerebral cortex and in- clude the thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and ac- cumbens structures. These bilateral structures – symmetrically located within the left…

DOCTORAL THESIS: Deep learning methods for automated detection of new multiple sclerosis lesions in longitudinal magnetic resonance images

By Mostafa Salem Supervised by Prof. Joaquim Salvi / Prof. Xavier Lladó   Abstract Multiple sclerosis (MS) is an inflammatory disease of the central nervous system, which is characterized by the presence of lesions in the brain and the spinal cord. Magnetic resonance imaging (MRI) has become a core para-clinical tool…