Deep learning approaches for MRI-based labelling of neuroanatomical structures following epilepsy surgery
The aim of this project is to develop deep learning models to automatically label resected brain regions and other related neuroanatomical structures using a large dataset (n = 697) of postsurgical MRI scans. Students will work with clinically acquired imaging data and modern machine-learning methods to develop AI tools that address real-world problems in epilepsy surgery and neuroimaging analysis.
This project would be well suited to students with an interest in data science, neuroimaging and artificial intelligence techniques.
Aim
- Train a deep learning model to label resected brain regions of postsurgical MRI scans.
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