Background Magnetic resonance (MR) relaxometry is of increasing scientific relevance in neurodegenerative disorders but is still not established in clinical routine. Several studies have investigated relaxation time alterations in disease-specific areas in Parkinson's disease (PD), all using manually drawn regions of interest (ROI). Implementing MR relaxometry into the clinical setting involves the reduction of time needed for postprocessing using an investigator-independent and reliable approach. The aim of this study was to evaluate an automated, atlas-based ROI method for evaluating T2* relaxation times in patients with PD. Method Automated atlas-based ROI analysis of quantitative T2* maps were generated from 20 PD patients and 20 controls. To test for the accuracy of the atlas-based ROI segmentation, we evaluated the spatial overlap in comparison with manually segmented ROIs using the Dice similarity coefficient (DSC). Additionally, we tested for group differences using our automated atlas-based ROIs of the putamen, globus pallidus, and substantia nigra. Results A good spatial overlap accuracy was shown for the automated segmented putamen (mean DSC, 0.64 ± 0.04) and was inferior but still acceptable for the substantia nigra (mean DSC, 0.50 ± 0.17). Based on our automated defined ROI selection, a significant decrease of T2* relaxation time was found in the putamen as well as in the internal and external globus pallidus in PD patients compared with healthy controls. Conclusion Automated digital brain atlas-based approaches are reliable, more objective and time-efficient, and therefore have the potential to replace the time-consuming manual drawing of ROIs.