Generation of synthetic training data for SEEG electrodes segmentation.
Fiche publication
Date publication
mars 2022
Journal
International journal of computer assisted radiology and surgery
Auteurs
Membres identifiés du Cancéropôle Est :
Pr WEMMERT Cédric
Tous les auteurs :
Pantovic A, Ren X, Wemmert C, Ollivier I, Essert C
Lien Pubmed
Résumé
Stereoelectroencephalography (SEEG) is a minimally invasive surgical procedure, used to locate epileptogenic zones. An accurate identification of the metallic contacts recording the SEEG signal is crucial to ensure effectiveness of the upcoming treatment. However, due to the presence of metal, post-operative CT scans contain strong streak artefacts that interfere with deep learning segmentation algorithms and require a lot of training data to distinguish from actual contacts. We propose a method to generate synthetic data and use them to train a neural network to precisely locate SEEG electrode contacts.
Mots clés
Data augmentation, Epilepsy, Radon transform, Segmentation, Sinogram, Stereoelectroencephalography
Référence
Int J Comput Assist Radiol Surg. 2022 Mar 11;: