Comparison of interactive and automatic segmentation of stereoelectroencephalography electrodes on computed tomography post-operative images: preliminary results.
Sahar BenadiIrene OllivierCaroline EssertPublished in: Healthcare technology letters (2018)
Stereoelectroencephalography is a surgical procedure used in the treatment of pharmacoresistant epilepsy. Multiple electrodes are inserted in the patient's brain in order to record the electrical activity and detect the epileptogenic zone at the source of the seizures. An accurate localisation of their contacts on post-operative images is a crucial step to interpret the recorded signals and achieve a successful resection afterwards. In this Letter, the authors propose interactive and automatic methods to help the surgeon with the segmentation of the electrodes and their contacts. Then, they present a preliminary comparison of the methods in terms of accuracy and processing time through experimental measurements performed by two users, and discuss these first results. The final purpose of this work is to assist the neurosurgeons and neurologists in the contacts localisation procedure, make it faster, more precise and less tedious.
Keyphrases
- deep learning
- convolutional neural network
- computed tomography
- reduced graphene oxide
- machine learning
- carbon nanotubes
- minimally invasive
- solid state
- magnetic resonance imaging
- positron emission tomography
- case report
- high resolution
- white matter
- magnetic resonance
- resting state
- mass spectrometry
- temporal lobe epilepsy
- neural network
- blood brain barrier
- cerebral ischemia
- subarachnoid hemorrhage