Subthalamic Nucleus segmentation in high-field Magnetic Resonance data. Is space normalization by template co-registration necessary?

TL;DR

This study compares deep learning-based segmentation of the subthalamic nucleus in high-field MRI, finding native space segmentation outperforms template-based methods for STN, with implications for pre-surgical planning in Parkinson's Disease.

Contribution

It demonstrates that direct segmentation in native MRI space yields better STN segmentation results than template-based approaches, challenging the necessity of space normalization.

Findings

Native space segmentation outperforms template-based for STN.

No significant difference for Red Nucleus and Substantia Nigra.

High-field MRI improves detailed brain structure segmentation.

Abstract

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).