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Geodesic Regression of Image and Shape Data for Improved Modeling of 4D Trajectories

1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
2INRIA/ICM, Pitié SalpêtrièrE Hospital, Paris, France.
ISBI 2014
Publication Date:
Proc IEEE Int Symp Biomed Imaging
Volume Number:
Proc IEEE Int Symp Biomed Imaging. 2014 Apr;2014:385-8.
PubMed ID:
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., Prastawa M., Gerig G., Durrleman S. Geodesic Regression of Image and Shape Data for Improved Modeling of 4D Trajectories. Proc IEEE Int Symp Biomed Imaging. 2014 Apr;2014:385-8. PMID: 25356192. PMCID: PMC4209724.
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A variety of regression schemes have been proposed on images or shapes, although available methods do not handle them jointly. In this paper, we present a framework for joint image and shape regression which incorporates images as well as anatomical shape information in a consistent manner. Evolution is described by a generative model that is the analog of linear regression, which is fully characterized by baseline images and shapes (intercept) and initial momenta vectors (slope). Further, our framework adopts a control point parameterization of deformations, where the dimensionality of the deformation is determined by the complexity of anatomical changes in time rather than the sampling of the image and/or the geometric data. We derive a gradient descent algorithm which simultaneously estimates baseline images and shapes, location of control points, and momenta. Experiments on real medical data demonstrate that our framework effectively combines image and shape information, resulting in improved modeling of 4D (3D space + time) trajectories.

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