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Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls (n=105) and patients with PD (n=105), multiple systemic atrophy (n=132), and progressive supranuclear palsy (n=69) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotating data for DL models, the representative V-Net and UNETR. The Dice scores and area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated. The segmentation times of V-Net and UNETR for the six brain structures per patient were 3.48 +- 0.17 and 48.14 +- 0.97 s, respectively, being at least 300 times faster than FS (15,735 +- 1.07 s). Dice scores of both DL models were sufficiently high (>0.85), and their AUCs for disease classification were superior to that of FS. For classification of normal vs. P-plus and PD vs. multiple systemic atrophy (cerebellar type), the DL models and FS showed AUCs above 0.8. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research., Joomee Song and Juyoung Hahm contributed equally to this work as the co-first author. Jong Hyeon Ahn and Kyung-Su Kim (kskim.doc@gmail.com) contributed equally to this work as the co-corresponding author

This is an author response to the letter by doctors Nicholas Doher and Harsh V Gupta. Despite the narrow indication for the dopamine transporter (DAT) scan to distinguish essential tremor from Parkinsonian syndromes, as approved by the Food and Drug Administration (FDA), DAT imaging is utilised for various other clinical symptoms in patients with parkinsonism. DAT scan is the only non-invasive modality developed to assess functional integrity of the dopaminergic system currently used in clinical practice, and could give valuable clinical information for selected patients. The authors believe that the approved indication of the DAT scan will be broadened to meet its versatile diagnostic potential.