Abstract #4911

Automatic Fetal Orientation Detection Algorithm in Fetal MRI

Joshua Eisenstat¹, Matthias Wagner², Logi Vidarsson³, Birgit Ertl-Wagner^2,4, and Dafna Sussman^1,5,6

¹Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University, Toronto, ON, Canada, ²Division of Neuroradiology, The Hospital for Sick Children, Toronto, ON, Canada, ³Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada, ⁴Department of Medical Imaging, Faculty of Medicine, University of Toronto, Toronto, ON, Canada, ⁵Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University and St. Michael’s Hospital, Toronto, ON, Canada, ⁶Department of Obstetrics and Gynecology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, Convolutional Neural Networks, Fet-NetFetal orientation determines the mode of delivery. It is also important for sequence planning in fetal MRI. This abstract proposes Fet-Net, a deep-learning algorithm, which uses a novel convolutional neural network (CNN) architecture, to automatically detect fetal orientation from a 2-dimensional (2D) magnetic resonance imaging (MRI) slice. 6,120 2D MRI slices displaying vertex, breech, oblique and transverse fetal orientations were used for training, validation and testing. Fet-Net achieved an average accuracy and F1 score of 97.68%, and a loss of 0.06828. Fet-Net was able to detect and classify fetal orientation, which may serve to accelerate fetal MRI acquisition.

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