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.
Deep brain stimulation (DBS) is an interventional treatment for Parkinson’s disease in which electrodes are placed into specific locations in the basal ganglia to alleviate symptoms such as tremor and dyskinesia. Due to the small size and low contrast of specific targets of interest, such as the subthalamic nucleus (STN), localisation of these structures from pre-operative MRI is of great value. These localisation approaches are often atlas-based, using registration algorithms to align patient images with a prior annotated atlas. However, these methods require a large amount of time, especially for correctly estimating deformation fields, or are prone to error for smaller structures such as the STN. This paper investigates two deep learning frameworks for the localisation of the stn from T1- and T2-weighted MRI using convolutional neural networks which estimate its centroid. These methods are compared against an atlas-based segmentation using the ParkMedAtlis v3 atlas, showing an improvement in localisation error in the range of ≈0.5-1.3 mm with a reduction of orders of magnitude of computation time. This method of STN localisation will allow us in future to automatically identify the STN for DBS surgical planning as well as define a greatly reduced region-of-interest for more accurate segmentation of the STN.
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