| Grant number: | 16/18332-8 |
| Support Opportunities: | Scholarships in Brazil - Master |
| Start date: | January 01, 2017 |
| End date: | September 30, 2018 |
| Field of knowledge: | Engineering - Electrical Engineering |
| Principal Investigator: | Roberto de Alencar Lotufo |
| Grantee: | Oeslle Alexandre Soares de Lucena |
| Host Institution: | Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| Associated scholarship(s): | 17/23747-5 - Deep-learning-based tractography for surgical planning in epilepsy treatment, BE.EP.MS |
Abstract The necessity to develop fast, robust and accurate brain MR segmentation tools that require few parametersto adjust is essential since manual segmentation performed by neurologists is a time-consuming task. Among automaticmethods, Deep Learning approaches have outperformed the state-of-the-art in many computer vision problems. DeepLearning techniques major disadvantage is that they usually require large amounts of labeled data for training, which isnot available in medical image segmentation problems.This M.Sc. research project intends to investigate the use of Deep Learning techniques applied to brainstructure segmentation in MR imaging. Two specic applications will be investigated: the brain extraction, also knownas skull-stripping, and corpus callosum (CC) segmentation problems. Our method aims to use the Simultaneous Truth andPerformance Level Estimation (STAPLE) algorithm to generate labeled data and use it as input for a Convolutional NeuralNetwork (CNN). We will compare our methodology with the state-of-the-art techniques for brain and CC segmentation. | |
| News published in Agência FAPESP Newsletter about the scholarship: | |
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