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• W2804579648 abstract "In this paper, we present a fully-automated subcortical and ventricular shape generation pipeline that acts on structural magnetic resonance images (MRIs) of the human brain. Principally, the proposed pipeline consists of three steps: 1) automated structure segmentation using the diffeomorphic multi-atlas likelihood-fusion algorithm; 2) study-specific shape template creation based on the Delaunay triangulation; 3) deformation-based shape filtering using the large deformation diffeomorphic metric mapping for surfaces. The proposed pipeline is shown to provide high accuracy, sufficient smoothness, and accurate anatomical topology. Two datasets focused upon Huntington’s disease (HD) were used for evaluating the performance of the proposed pipeline. The first of these contains a total of 16 MRI scans, each with a gold standard available, on which the proposed pipeline’s outputs were observed to be highly accurate and smooth when compared with the gold standard. Visual examinations and outlier analyses on the second dataset, which contains a total of 1445 MRI scans, revealed 100% success rates for the putamen, the thalamus, the globus pallidus, the amygdala, and the lateral ventricle in both hemispheres and rates no smaller than 97% for the bilateral hippocampus and caudate. Another independent dataset, consisting of 15 atlas images and 20 testing images, was also used to quantitatively evaluate the proposed pipeline, with high accuracy having been obtained. In short, the proposed pipeline is herein demonstrated to be effective, both quantitatively and qualitatively, using a large collection of MRI scans." @default.
• W2804579648 created "2018-06-01" @default.
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• W2804579648 date "2018-05-17" @default.
• W2804579648 modified "2023-10-15" @default.
• W2804579648 title "A Fully-Automated Subcortical and Ventricular Shape Generation Pipeline Preserving Smoothness and Anatomical Topology" @default.
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• W2804579648 doi "https://doi.org/10.3389/fnins.2018.00321" @default.
• W2804579648 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5966575" @default.
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• W2804579648 hasPublicationYear "2018" @default.
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