Automated segmentation of midbrain nuclei using deep learning and multisequence MRI: A longitudinal study on iron accumulation with age

Abstract

Elevated levels of brain iron, particularly within the basal ganglia, have been associated with cognitive and motor impairment in normal aging and neurodegenerative conditions. The subthalamic nucleus (STN), substantia nigra (SN), and red nucleus (RN), despite their high iron content and contribution to motor and cognitive processes, are less frequently studied. This oversight can largely be attributed to the challenges posed by in-vivo assessments of these small, deep-seated midbrain structures. We developed and validated an automated tool for the segmentation of the STN, SN, and RN. Multi-sequence magnetic resonance imaging (MRI) data including T1-weighted, FLAIR, Quantitative Susceptibility Mapping (QSM) and R2* alongside manual delineation on QSM images of 40 individuals, was used to train segmentation models based on nnU-Net deep-learning framework. A combination of QSM and FLAIR sequences was found to be optimal for structure segmentation (mean Dice scores of 0.84, 0.91 and 0.94 for STN, SN and RN, respectively). We next applied the automated segmentation method to an independent 3-year longitudinal dataset including 175 healthy adults (age range at baseline: 20-79 years old). Structural equation modelling was used to assess iron accumulation over time using age, sex, baseline iron and regional volume as factors of interest. Cross-sectionally, older age was linearly associated with higher iron load in SN and STN; the association was non-linear in RN. Longitudinally, results indicated significant iron accumulation in the STN (Mean increase = 0.02, p = 0.005) and SN (Mean increase = 0.035, p = 0.001), but not in the RN (Mean increase = 0.015, p = 0.2). Our findings demonstrated high performance of nnU-Net in automated segmentation, and advanced our understanding of iron accumulation in midbrain nuclei in aging.