Automated Segmentation Of Substantia Nigra And Red Nucleus Using Quantitative Susceptibility Mapping Images: Application To Parkinson'S Disease

Accurate segmentation of substantia nigra (SN) and red nucleus (RN) is challenging, yet important for understanding health problems like Parkinson's disease (PD). This paper proposes an algorithm to segment SN and RN from quantitative susceptibility mapping (QSM) MRI and use the results to investigate PD. Algorithm-derived segments (based on level set and watershed transform) are compared to expert manually-derived segmentations in 40 participants. Using Bayesian regression models, we compare QSM values between PD and control groups, and investigate relationships with global cognitive ability and motor severity in PD. The proposed algorithm produces high quality segmentations, validated against expert manual segmentation. We show moderate evidence of increased QSM values in SN in PD relative to controls, with moderate evidence for association between QSM, global cognitive ability, and motor impairment in the SN in PD. We suggest an improved midbrain segmentation algorithm may be useful for monitoring iron-related disease severity in Parkinson's.