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Robust Automated Constellation-based Landmark Detection in Human Brain Imaging

Institution:
1Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA.
2Department of Psychiatry, University of Iowa Hospitals & Clinics, Iowa City, IA, USA.
Publisher:
Elsevier Science
Publication Date:
Apr-2018
Journal:
Neuroimage
Volume Number:
170
Pages:
471-81
Citation:
Neuroimage. 2018 Apr 15;170:471-81.
PubMed ID:
28392490
PMCID:
PMC5630513
Keywords:
Automated landmark detection , Morphometric measures, Principle component analysis, Statistical shape models
Appears in Collections:
NA-MIC
Sponsors:
R01 NS040068/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
R01 EB020062/EB/NIBIB NIH HHS/United States
U01 NS083223/NS/NINDS NIH HHS/United States
S10 RR023392/RR/NCRR NIH HHS/United States
Generated Citation:
Ghayoor A., Vaidya J.G., Johnson H.J. Robust Automated Constellation-based Landmark Detection in Human Brain Imaging. Neuroimage. 2018 Apr 15;170:471-81. PMID: 28392490. PMCID: PMC5630513.
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A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.