Incremental kernel ridge regression for the prediction of soft tissue deformations

  • Authors:

  • Binbin Pan;James J. Xia;Peng Yuan;Jaime Gateno;Horace H. S. Ip;Qizhen He;Philip K. M. Lee;Ben Chow;Xiaobo Zhou

  • Affiliations:

  • The Methodist Hospital Research Institute, Houston, Texas, USA, School of Mathematics and Computational Science, Sun Yat-Sen University, China;The Methodist Hospital Research Institute, Houston, Texas;The Methodist Hospital Research Institute, Houston, Texas;The Methodist Hospital Research Institute, Houston, Texas;Department of Computer Science, City University of Hong Kong, Hong Kong, China;Department of Computer Science, City University of Hong Kong, Hong Kong, China;Hong Kong Dental Implant & Maxillofacial Centre, Hong Kong, China;Hong Kong Dental Implant & Maxillofacial Centre, Hong Kong, China;The Methodist Hospital Research Institute, Houston, Texas

  • Venue:
  • MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
  • Year:
  • 2012

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Abstract

This paper proposes a nonlinear regression model to predict soft tissue deformation after maxillofacial surgery. The feature which served as input in the model is extracted with Finite Element Model (FEM). The output in the model is the facial deformation calculated from the preoperative and postoperative 3D data. After finding the relevance between feature and facial deformation by using the regression model, we establish a general relationship which can be applied to all the patients. As a new patient comes, we predict his/her facial deformation by combining the general relationship and the new patient's biomechanical properties. Thus, our model is biomechanical relevant and statistical relevant. Validation on eleven patients demonstrates the effectiveness and efficiency of our method.