As a Research Assistant to Dr. Davis at Penn’s Center for Neuroengineering and Therapeutics, Hannah Gonzalez worked alongside Dr. Arnold to train a deep learning model to automate resection cavity segmentation on postoperative MRI of epilepsy patients to help physicians quantify removed brain structures.
The purpose of the project was to expand on a previous paper about Deep Learning-Based Automated Segmentation of Resection Cavities on Postsurgical Epilepsy MRI by adding a step in the data preprocessing and training of the model. After training 3 different models and running inference and the majority vote algorithm, it was determined that the Axial/Coronal model is the highest performer.
Future work in investigating the sagittal model to see how its performance can be improved.
Read more about this project HERE.