MRI-texture features in glioblastoma: Applications of Machine learning.
Sala de Juntas - ETSI Industriales
Jueves 18 de Febrero del 2016
Grupo de Oncología Matemática
David Molina, 
 
Sala de Juntas - ETSI Industriales
Miércoles 18 de Febrero de 2016, 12:30
 
MRI-texture features in glioblastoma: Applications of Machine learning.
 
This talk presents some novel research articles based on the applications of texture analysis in MRI sets of glioblastoma (GBM) images. GBM is the most common and aggressive primary high-grade brain tumor, characterized by its high heterogeneity, with elevated cellular densities, vascular proliferation and necrosis, showing a wide range of histological variations. Median overall survival for GBM patients is only 12-15 months.
 
Texture analysis can be defined as a method for quantifying the spatial distribution of voxel intensities in a set of images. It has many applications in a wide number of fields and many different methods have been developed over the past few decades.
 
Machine learning algorithms could be used to empirically validate the obtained texture analysis results.