Segmentation of intracranial electrode contacts using convolutional neural networks

For patients with intractable epilepsy the surgical placement of intracranial electrodes can better localize the seizure onset zone. Stereoelectroencephalography (SEEG) is one technique, where depth electrodes made of multiple contacts record activity in the brain. The precise interpretation of recordings requires the anatomical localization of each contact. Contact positions can be manually localized or determined using semi-automated algorithms. This thesis works towards the automation of SEEG contact localization with a 3D U-Net, a deep learning architecture optimized for biomedical image segmentation. The first chapter will introduce the clinical workflow for SEEG, available tools, and the potential role of deep learning. The second chapter will cover the proposed algorithm and validation methods. The last two chapters will present the accuracy of the U-Net in contrast to approaches currently employed in the clinic. Overall, the error and accuracy of the proposed method compares favorably in an independent set of clinical data. Future work will look to continue to optimize performance.