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Stereoelectroencephalography (SEEG) is a minimally invasive surgical procedure, used in the treatment of pharmacoresistant epilepsy to precisely locate areas of the brain where seizures originate. An accurate localization of SEEG electrodes is crucial to design a resection plan before surgically removing epileptogenic zone. We propose to train a deep neural network to accurately segment electrode contacts without making any manual adjustments. We trained a 2D and a 3D version of the U-Net1, 2 neural network architecture to handle this task, taking postoperative CT scans as input. We evaluated our models on 18 image datasets of patients using different metrics, and provided a comparison of the two approaches. The presented models are robust to electrode bending and do not need any prior information to make quick and accurate predictions. To the best of our knowledge, deep learning has not been used yet for this task.
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