Journal article
A Fully-Automated Subcortical and Ventricular Shape Generation Pipeline Preserving Smoothness and Anatomical Topology
Frontiers in neuroscience, Vol.12(MAY), pp.321-321
05/17/2018
DOI: 10.3389/fnins.2018.00321
PMCID: PMC5966575
PMID: 29867332
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 1,445 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.
Details
- Title: Subtitle
- A Fully-Automated Subcortical and Ventricular Shape Generation Pipeline Preserving Smoothness and Anatomical Topology
- Creators
- Xiaoying Tang - Sun Yat-sen UniversityYuan Luo - Sun Yat-sen UniversityZhibin Chen - Sun Yat-sen UniversityNianwei Huang - Sun Yat-sen UniversityHans J Johnson - University of IowaJane S Paulsen - University of IowaMichael I Miller - Johns Hopkins University
- Resource Type
- Journal article
- Publication Details
- Frontiers in neuroscience, Vol.12(MAY), pp.321-321
- DOI
- 10.3389/fnins.2018.00321
- PMID
- 29867332
- PMCID
- PMC5966575
- NLM abbreviation
- Front Neurosci
- ISSN
- 1662-4548
- eISSN
- 1662-453X
- Publisher
- Frontiers Media S.A
- Grant note
- 81501546 / National Natural Science Foundation of China
- Language
- English
- Date published
- 05/17/2018
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry; Psychological and Brain Sciences
- Record Identifier
- 9984185371802771
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