Epilepsy, Temporal Lobe Clinical Trial
Official title:
Symmetricity-Driven Learning Framework for Pediatric Temporal Lobe Epilepsy Detection Using 18F-FDG-PET Imaging
This study aims to use radiomics analysis and deep learning approaches for seizure focus detection in pediatric patients with temporal lobe epilepsy (TLE). Ten positron emission tomograph (PET) radiomics features related to pediatric temporal bole epilepsy are extracted and modelled, and the Siamese network is trained to automatically locate epileptogenic zones for assistance of diagnosis.
Purpose:The key to successful epilepsy control involves locating epileptogenic focus before
treatment. 18F-FDG PET has been considered as a powerful neuroimaging technology used by
physicians to assess patients for epilepsy. However, imaging quality, viewing angles, and
experiences may easily degrade the consistency in epilepsy diagnosis. In this work, the
investigators develop a framework that combines radiomics analysis and deep learning
techniques to a computer-assisted diagnosis (CAD) method to detect epileptic foci of
pediatric patients with temporal lobe epilepsy (TLE) using PET images.
Methods:Ten PET radiomics features related to pediatric temporal bole epilepsy are first
extracted and modelled. Then a neural network called Siamese network is trained to quanti-fy
the asymmetricity and automatically locate epileptic focus for diagnosis.The performance of
the proposed framework was tested and compared with both the state-of-art clinician software
tool and human physicians with different levels of experiences to validate the accuracy and
consistency.
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