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Geodesic Shape Regression with Multiple Geometries and Sparse Parameters

Department of Computer Science and Engineering, NYU Tandon School of Engineering, NY, USA. Electronic address:
Elsevier Science
Publication Date:
Med Image Anal
Volume Number:
Med Image Anal. 2017 Jul;39:1-17.
PubMed ID:
4D shape modeling, Geodesic, LDDMM, Spatiotemporal, Multi-object complex, Shape regression
Appears in Collections:
R01 HD055741/HD/NICHD NIH HHS/United States
U01 NS082086/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Fishbaugh J., Durrleman S., Prastawa M., Gerig G. Geodesic Shape Regression with Multiple Geometries and Sparse Parameters. Med Image Anal. 2017 Jul;39:1-17. PMID: 28399476. PMCID: PMC6016554.
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Many problems in medicine are inherently dynamic processes which include the aspect of change over time, such as childhood development, aging, and disease progression. From medical images, numerous geometric structures can be extracted with various representations, such as landmarks, point clouds, curves, and surfaces. Different sources of geometry may characterize different aspects of the anatomy, such as fiber tracts from DTI and subcortical shapes from structural MRI, and therefore require a modeling scheme which can include various shape representations in any combination. In this paper, we present a geodesic regression model in the large deformation (LDDMM) framework applicable to multi-object complexes in a variety of shape representations. Our model decouples the deformation parameters from the specific shape representations, allowing the complexity of the model to reflect the nature of the shape changes, rather than the sampling of the data. As a consequence, the sparse representation of diffeomorphic flow allows for the straightforward embedding of a variety of geometry in different combinations, which all contribute towards the estimation of a single deformation of the ambient space. Additionally, the sparse representation along with the geodesic constraint results in a compact statistical model of shape change by a small number of parameters defined by the user. Experimental validation on multi-object complexes demonstrate robust model estimation across a variety of parameter settings. We further demonstrate the utility of our method to support the analysis of derived shape features, such as volume, and explore shape model extrapolation. Our method is freely available in the software package deformetrica which can be downloaded at