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A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
Published on: May 20, 2016
Neuroimaging-Based Deep Learning Applications for Lesion Detection and Predicting the Outcome Following Epilepsy
Merran R Courtney1, Benjamin Sinclair1, Benjamin H Brinkmann2
1Department of Neuroscience, School of Translational Medicine, Monash University, 99 Commercial Road, Melbourne, VIC 3004, Australia; Department of Neurology, Alfred Health, Melbourne, Victoria, Australia.
None:
Neuroimaging studies are essential for evaluating patients with drug-resistant focal epilepsy and determining their candidacy for epilepsy surgery. The past decade has seen the emergence of neuroimaging-based deep learning models, which have been developed to both detect epileptogenic lesions on MR imaging and predict post-surgical seizure outcome. Large, multi-center studies have demonstrated promise for epileptogenic lesion detection; however, neuroimaging-based surgical outcome prediction models remain exploratory. Translation of such models into routine epilepsy surgery clinical workflows will require transparent, interpretable, and prospectively validated model designs.
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