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Graph learning for MRI semantic segmentation

Grant number: 19/16112-9
Support type:Scholarships abroad - Research Internship - Master's degree
Effective date (Start): December 01, 2019
Effective date (End): March 29, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal researcher:Roberto Marcondes Cesar Junior
Grantee:Larissa de Oliveira Penteado
Supervisor abroad: Isabelle Bloch
Home Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Research place: ParisTech, France  
Associated to the scholarship:18/07386-5 - Segmentation of neonatal magnetic resonance imaging: a structural approach, BP.MS

Abstract

The semantic segmentation of Magnetic Resonance Imaging (MRI) plays an important role in diseases diagnosis, treatment and development follow-up. The manual analysis of such exams is time-consuming and prone to user variability. Therefore, over the years different automatic and semi-automatic methods have been developed. Most of them were created to deal with adult data. Because of intrinsic image differences of these volumes to neonates and infants ones, such methods tend to fail for these patients. So, in this project, we aim to develop a robust algorithm to perform the semantic segmentation of brain MRI of children and neonates, by using Graphs and Graph Neural Networks. (AU)

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