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4D Continuous Medial Representation by Geodesic Shape Regression

Institution:
Computer Science and Engineering, Tandon School of Engineering, New York University, NY, USA.
Publication Date:
Apr-2018
Journal:
Proc IEEE Int Symp Biomed Imaging
Volume Number:
2018
Pages:
1014-7
Citation:
Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1014-7.
PubMed ID:
29973975
PMCID:
PMC6027751
Keywords:
Brain , Modeling - Anatomical, Shape Analysis, physiological and pathological
Appears in Collections:
NA-MIC
Sponsors:
R01 EB021391/EB/NIBIB NIH HHS/United States
R01 NS040068/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
U01 NS082086/NS/NINDS NIH HHS/United States
R01 DA038215/DA/NIDA NIH HHS/United States
Generated Citation:
Hong S., Fishbaugh J., Gerig G. 4D Continuous Medial Representation by Geodesic Shape Regression. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1014-7. PMID: 29973975. PMCID: PMC6027751.
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Longitudinal shape analysis has shown great potential to model anatomical processes from baseline to follow-up observations. Shape regression estimates a continuous trajectory of time-discrete anatomical shapes to quantify temporal changes. The need for shape alignment and point-to-point correspondences represent limitations of current shape analysis methodologies, and present significant challenges in shape evaluation. We propose a method that estimates a continuous trajectory of continuous medial representations (CM-Rep) from a set of time-discrete observed shapes. To avoid the traditional step of aligning individual objects, shape changes are modeled via diffeomorphic ambient space deformations. Using a medial shape representation, we separately capture object pose changes and intrinsic geometry changes. Tests and validation with synthetic and real anatomical shapes demonstrate that the new method captures extrinsic shape changes as well as intrinsic shape changes encoded with CM-Reps, a highly relevant property for studying growth and disease processes.