Conditional Variability of Statistical Shape Models Based on Surrogate Variables

Authors:
Rémi Blanc;Mauricio Reyes;Christof Seiler;Gábor Székely
Affiliations:
Computer Vision Laboratory, ETHZ, Zürich, Switzerland 8092;ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland 3014;Computer Vision Laboratory, ETHZ, Zürich, Switzerland 8092;Computer Vision Laboratory, ETHZ, Zürich, Switzerland 8092
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

We propose to increment a statistical shape model with surrogate variables such as anatomical measurements and patient-related information, allowing conditioning the shape distribution to follow prescribed anatomical constraints. The method is applied to a shape model of the human femur, modeling the joint density of shape and anatomical parameters as a kernel density. Results show that it allows for a fast, intuitive and anatomically meaningful control on the shape deformations and an effective conditioning of the shape distribution, allowing the analysis of the remaining shape variability and relations between shape and anatomy. The approach can be further employed for initializing elastic registration methods such as Active Shape Models, improving their regularization term and reducing the search space for the optimization.