  Deep Brain Stimulation (DBS) is one of the most successful methods to
diminish late-stage Parkinson's Disease (PD) symptoms. It is a delicate
surgical procedure which requires detailed pre-surgical patient's study.
High-field Magnetic Resonance Imaging (MRI) has proven its improved capacity of
capturing the Subthalamic Nucleus (STN) - the main target of DBS in PD - in
greater detail than lower field images. Here, we present a comparison between
the performance of two different Deep Learning (DL) automatic segmentation
architectures, one based in the registration to a brain template and the other
performing the segmentation in in the MRI acquisition native space. The study
was based on publicly available high-field 7 Tesla (T) brain MRI datasets of
T1-weighted and T2-weighted sequences. nnUNet was used on the segmentation step
of both architectures, while the data pre and post-processing pipelines
diverged. The evaluation metrics showed that the performance of the
segmentation directly in the native space yielded better results for the STN
segmentation, despite not showing any advantage over the template-based method
for the to other analysed structures: the Red Nucleus (RN) and the Substantia
Nigra (SN).
