Dynamic Atlas-Based Segmentation and Quantification of Neuromelanin-Rich Brainstem Structures in Parkinson Disease
Product Description
Abstract:
Parkinson’s disease (PD) is a neurodegenerative disease histo-pathologically characterized by neuronal loss, Lewy bodies, and depigmentation of two paired neuromelanin (NM) rich brainstem structures: the substantial nigra pars compacta (SNc) and the locus coeruleus (LC). In spite of the aforementioned cellular hallmarks, the clinical diagnosis of PD still relies on the identification of motor symptoms that appear at a late clinical stage, when there is already significant cell loss in the SNc and LC.
To quantify these differences, early methods were based on visual inspection, or manual contour drawing, followed by quantification of the volume and/or the intensity of NM-rich structures on neuromelanin-sensitive T1-weighted images. The quantification results obtained with our dynamic atlas to train an Artificial Neural Network in the task of classifying healthy and PD patients based on the quantification of their NMRS in NM MRI images of their brains. In this proposed system the MRI brain images is given as input. Then the active contour segmentation method is used in order to detect the affected portion of Parkinson disease. The detected tumor part is trained by Faster Convolution neural network and finally 3d segmented structure is obtained.
Proposed System:
In this proposed system the MRI brain images is given as input. Then the active contour segmentation method is used in order to detect the affected portion of Parkinson disease. Multi image atlas segmentation offers superior segmentation accuracy over single atlas and model-based average representations, at a high computational cost. The detected tumor part is trained by Faster Convolution neural network and finally 3d segmented structure is obtained.
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Mini Projects - Hardware includes
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The Delivery time for Hardware Mini projects is 7-8 working days.