Automated Deep Learning-based Segmentation of Brain, SEEG and DBS Electrodes on CT Images

Stereoelectroencephalography (sEEG) and deep brain stimulation (DBS) are effective surgical diagnostic and therapeutic procedures of the depth electrodes implantation in the brain. The benefit and outcome of these procedures directly depend on the electrode placement. Our goal was to accurately segment and visualize electrode position after the sEEG and DBS procedures. We trained a deep learning network to automatically segment electrodes trajectories and brain tissue from postsurgical CT images. We used 90 head CT scans that include intracerebral electrodes and their corresponding segmentation masks to train, validate and test the model. Mean accuracy and dice score in 5-fold cross-validation for the 3D-cascade U-Net model were 0.99 and 0.92, respectively. When the network was tested on an unseen test set, the dice overlap with the manual segmentations was 0.89. In this paper, we present a deep-learning approach for automatic patient-specific delineation of the brain, the sEEG and DBS electrodes from different varying quality of CT images. This robust method may inform on the postsurgical electrode positions fast and accurately. Moreover, it is useful as an input for neurosurgical and neuroscientific toolboxes and frameworks.