Coupling CRFs and deformable models for 3D medical image segmentation

Gabriel Tsechpenakis, Jianhua Wang, Brandon Mayer, Dimitris Metaxas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

In this paper we present a hybrid probabilistic framework for 3D image segmentation, using Conditional Random Fields (CRFs) and implicit deformable models. Our 3D deformable model uses voxel intensity and higher scale textures as data-driven terms, while the shape is formulated implicitly using the Euclidean distance transform. The data-driven terms are used as observations in a 3D discriminative CRF, which drives the model evolution based on a simple graphical model. In this way, we solve the model evolution as a joint MAP estimation problem for the 3D label field of the CRF and the 3D shape of the deformable model. We demonstrate the performance of our approach in the estimation of the volume of the human tear menisci from images obtained with Optical Coherence Tomography.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
DOIs
StatePublished - Dec 1 2007
Event2007 IEEE 11th International Conference on Computer Vision, ICCV - Rio de Janeiro, Brazil
Duration: Oct 14 2007Oct 21 2007

Other

Other2007 IEEE 11th International Conference on Computer Vision, ICCV
CountryBrazil
CityRio de Janeiro
Period10/14/0710/21/07

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ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Tsechpenakis, G., Wang, J., Mayer, B., & Metaxas, D. (2007). Coupling CRFs and deformable models for 3D medical image segmentation. In Proceedings of the IEEE International Conference on Computer Vision [4409151] https://doi.org/10.1109/ICCV.2007.4409151