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Abstract #2417

Implementation of a convolutional neural network for brainstem landmark detection and co-registration

Owen Bleddyn Woodward1, Ian Driver1, Michael Germuska1, and Richard Wise2
1CUBRIC, Cardiff University, Cardiff, United Kingdom, 2Department of Neuroscience, Imaging and Clinical Science, 'G. D'Annunzio University' of Chieti-Pescara, Chieti, Italy

Synopsis

Keywords: Data Processing, Machine Learning/Artificial Intelligence, Co-registrationAccurate brainstem co-registration is important when analysing brainstem functional MRI data. We trained a convolutional neural network (CNN) to detect a set of brainstem landmarks and to define a brainstem region-of-interest and used these to co-register the brainstem between functional and anatomical space using previously developed landmark-based and automated brainstem co-registration (LBC and ABC) methods. The use of CNNs to supply these features to LBC and ABC produced results that compared well to conventional methods. Similar CNNs could be applied to other brain regions and such methods may be useful to automate the analysis of large functional datasets.

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Keywords