Segmentation of the subthalamic nucleus in MRI via Convolutional Neural Networks for deep brain stimulation planning
Résumé
Parkinson's disease is a neurodegenerative disorder affecting the basal ganglia and resulting in characteristic motor and non-motor symptoms. Although pharmocological treatments are often used, deep brain stimulation can be used either to complement these treatments or replace them if ineffective. Deep brain stimulation involves the implantation of electrodes into the patient's subcortical anatomy at particular regions of interest, such as the subthalamic nucleus, in order to control or alleviate abnormal neural behaviour. For these interventions to be successful, precise pre-operative segmentation of these structures in MRI is of paramount importance. This paper presents a convolutional neural network that is capable of learning the process of subthalamic nucleus segmentation from pre-operative clinical strength MR images with an accuracy of 58:2 ± 12:1% Dice which is within the Dice range of a one-voxel translation or dilation from the reference manual segmentation. This is the final step in a combined localisation/segmentation framework for small anatomy such as the STN which is computationally efficient (avoiding deformable registration) while simultaneously being easier for the user to correct in the presence of errors. © 2021 SPIE.