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We present a dynamic atlas composed of neuromelanin-enhanced magnetic resonance brain images of 40 healthy subjects. The performance of this atlas is evaluated on the fully automated segmentation of two paired neuromelanin-rich brainstem healthy structures: the substantia nigra pars compacta and the locus coeruleus. We show that our dynamic atlas requires in average 60% less images and, therefore, 60% less computation time than a static multi-image atlas while achieving a similar segmentation performance. Then, we show that by applying our dynamic atlas, composed of healthy subjects, to the segmentation and neuromelanin quantification of a set ofbrain images of 39 Parkinson disease patients, we are able to find significant quantitative differences in the level of neuromelanin between healthy subjects and Parkinson disease patients, thus opening the door to the use of these structures as image biomarkers in future computer aided diagnosis systems for the diagnosis of Parkinson disease.
parkinson disease; neuromelanin; magnetic resonance imaging; multi-image atlas based segmentation; neural network based classifier; parkinson disease; brain; brain mapping; image enhancement; image segmentation; medical computing; neurons; atlas-based segmentation; brainstem structures; computer aided diagnosis systems; magnetic resonance brain images; multi-images; segmentation performance; computer aided diagnosis; article; artificial neural network; brain stem; clinical article; controlled study; human; image analysis; image segmentation; locus ceruleus; nuclear magnetic resonance imaging; quantitative analysis; substantia nigra pars compacta