Substantia Nigra Segmentation in 7T MRI Using a 3D UNet Architecture for Enhanced Parkinson's Disease Diagnosis

High Resolution Magnetic Resonance Imaging provides detailed structural information of the soft tissues in the clinical environment. The Substantia Nigra (SN) plays a critical role in motor control and is a key region which can be affected in neurodegenerative diseases like Parkinson's disease. The precise segmentation of SN helps us in early diagnosis, treatment planning and disease progression. This study proposed a 3D UN et deep learning architecture for the segmentation of the Substantia Nigra. The suggested model was trained and validated with 7T Structural MRI scans ATAG dataset. The Quantitative analysis metrics like Dice Coefficient, Soft Dice Coefficient, Intersection over Union, Dice Loss, and Precision shows significant improvements in segmentation accuracy compared to the traditional segmentation algorithms, with implications for enhancing clinical workflows in the diagnosis and management of Parkinson syndromes.